{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tce3stUlHN0L"
      },
      "source": [
        "##### Copyright 2024 Google LLC."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "tuOe1ymfHZPu"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PkzOKBirz271"
      },
      "source": [
        "# Anomaly detection with embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFWzQEqNosrS"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/tutorials/anomaly_detection\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemini-api/tutorials/anomaly_detection.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemini-api/tutorials/anomaly_detection.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQPvHyHCz7mk"
      },
      "source": [
        "## Overview\n",
        "\n",
        "This tutorial demonstrates how to use the embeddings from the Gemini API to detect potential outliers in your dataset. You will visualize a subset of the 20 Newsgroup dataset using [t-SNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html){:.external} and detect outliers outside a particular radius of the central point of each categorical cluster.\n",
        "\n",
        "For more information on getting started with embeddings generated from the Gemini API, check out the [Python quickstart](https://ai.google.dev/tutorials/python_quickstart#use_embeddings).\n",
        "\n",
        "## Prerequisites\n",
        "\n",
        "You can run this quickstart in Google Colab.\n",
        "\n",
        "To complete this quickstart on your own development environment, ensure that your envirmonement meets the following requirements:\n",
        "\n",
        "-  Python 3.9+\n",
        "-  An installation of `jupyter` to run the notebook.\n",
        "\n",
        "## Setup\n",
        "\n",
        "First, download and install the Gemini API Python library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LyLLYVEhzud8"
      },
      "outputs": [],
      "source": [
        "!pip install -U -q google-generativeai"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z5GJi99k0Ctz"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "import tqdm\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import google.generativeai as genai\n",
        "\n",
        "# Used to securely store your API key\n",
        "from google.colab import userdata\n",
        "\n",
        "from sklearn.datasets import fetch_20newsgroups\n",
        "from sklearn.manifold import TSNE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yi0kitgd5aLG"
      },
      "source": [
        "### Grab an API Key\n",
        "\n",
        "Before you can use the Gemini API, you must first obtain an API key. If you don't already have one, create a key with one click in Google AI Studio.\n",
        "\n",
        "<a class=\"button button-primary\" href=\"https://makersuite.google.com/app/apikey\" target=\"_blank\" rel=\"noopener noreferrer\">Get an API key</a>\n",
        "\n",
        "In Colab, add the key to the secrets manager under the \"🔑\" in the left panel. Give it the name `API_KEY`.\n",
        "\n",
        "Once you have the API key, pass it to the SDK. You can do this in two ways:\n",
        "\n",
        "* Put the key in the `GOOGLE_API_KEY` environment variable (the SDK will automatically pick it up from there).\n",
        "* Pass the key to `genai.configure(api_key=...)`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6OeEZ5Bj5Zr8"
      },
      "outputs": [],
      "source": [
        "# Or use `os.getenv('API_KEY')` to fetch an environment variable.\n",
        "API_KEY=userdata.get('API_KEY')\n",
        "\n",
        "genai.configure(api_key=API_KEY)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ce6MdcP170Uv"
      },
      "source": [
        "Key Point: Next, you will choose a model. Any embedding model will work for this tutorial, but for real applications it's important to choose a specific model and stick with it. The outputs of different models are not compatible with each other.\n",
        "\n",
        "**Note**: At this time, the Gemini API is [only available in certain regions](https://ai.google.dev/available_regions)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h3mqsrUB7zsE"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "models/embedding-001\n",
            "models/embedding-001\n"
          ]
        }
      ],
      "source": [
        "for m in genai.list_models():\n",
        "  if 'embedContent' in m.supported_generation_methods:\n",
        "    print(m.name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qhWtEhZ6BO58"
      },
      "source": [
        "## Prepare dataset\n",
        "\n",
        "The [20 Newsgroups Text Dataset](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html){:.external} contains 18,000 newsgroups posts on 20 topics divided into training and test sets. The split between the training and test datasets are based on messages posted before and after a specific date. This tutorial uses the training subset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YtHABp9BBTIt"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['alt.atheism',\n",
              " 'comp.graphics',\n",
              " 'comp.os.ms-windows.misc',\n",
              " 'comp.sys.ibm.pc.hardware',\n",
              " 'comp.sys.mac.hardware',\n",
              " 'comp.windows.x',\n",
              " 'misc.forsale',\n",
              " 'rec.autos',\n",
              " 'rec.motorcycles',\n",
              " 'rec.sport.baseball',\n",
              " 'rec.sport.hockey',\n",
              " 'sci.crypt',\n",
              " 'sci.electronics',\n",
              " 'sci.med',\n",
              " 'sci.space',\n",
              " 'soc.religion.christian',\n",
              " 'talk.politics.guns',\n",
              " 'talk.politics.mideast',\n",
              " 'talk.politics.misc',\n",
              " 'talk.religion.misc']"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "newsgroups_train = fetch_20newsgroups(subset='train')\n",
        "\n",
        "# View list of class names for dataset\n",
        "newsgroups_train.target_names"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LPKgmQDQC3zd"
      },
      "source": [
        "Here is the first example in the training set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CSXYP0JwBXHh"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Lines: 15\n",
            "\n",
            " I was wondering if anyone out there could enlighten me on this car I saw\n",
            "the other day. It was a 2-door sports car, looked to be from the late 60s/\n",
            "early 70s. It was called a Bricklin. The doors were really small. In addition,\n",
            "the front bumper was separate from the rest of the body. This is \n",
            "all I know. If anyone can tellme a model name, engine specs, years\n",
            "of production, where this car is made, history, or whatever info you\n",
            "have on this funky looking car, please e-mail.\n",
            "\n",
            "Thanks,\n",
            "- IL\n",
            "   ---- brought to you by your neighborhood Lerxst ----\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "idx = newsgroups_train.data[0].index('Lines')\n",
        "print(newsgroups_train.data[0][idx:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Raafa2naC6Ec"
      },
      "outputs": [],
      "source": [
        "# Apply functions to remove names, emails, and extraneous words from data points in newsgroups.data\n",
        "newsgroups_train.data = [re.sub(r'[\\w\\.-]+@[\\w\\.-]+', '', d) for d in newsgroups_train.data] # Remove email\n",
        "newsgroups_train.data = [re.sub(r\"\\([^()]*\\)\", \"\", d) for d in newsgroups_train.data] # Remove names\n",
        "newsgroups_train.data = [d.replace(\"From: \", \"\") for d in newsgroups_train.data] # Remove \"From: \"\n",
        "newsgroups_train.data = [d.replace(\"\\nSubject: \", \"\") for d in newsgroups_train.data] # Remove \"\\nSubject: \"\n",
        "\n",
        "# Cut off each text entry after 5,000 characters\n",
        "newsgroups_train.data = [d[0:5000] if len(d) > 5000 else d for d in newsgroups_train.data]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZjE_Lsr6IhEd"
      },
      "outputs": [
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            "text/plain": [
              "                                                    Text  Label  \\\n",
              "0       WHAT car is this!?\\nNntp-Posting-Host: rac3.w...      7   \n",
              "1       SI Clock Poll - Final Call\\nSummary: Final ca...      4   \n",
              "2       PB questions...\\nOrganization: Purdue Univers...      4   \n",
              "3       Re: Weitek P9000 ?\\nOrganization: Harris Comp...      1   \n",
              "4       Re: Shuttle Launch Question\\nOrganization: Sm...     14   \n",
              "...                                                  ...    ...   \n",
              "11309    Re: Migraines and scans\\nDistribution: world...     13   \n",
              "11310  Screen Death: Mac Plus/512\\nLines: 22\\nOrganiz...      4   \n",
              "11311   Mounting CPU Cooler in vertical case\\nOrganiz...      3   \n",
              "11312   Re: Sphere from 4 points?\\nOrganization: Cent...      1   \n",
              "11313   stolen CBR900RR\\nOrganization: California Ins...      8   \n",
              "\n",
              "                     Class Name  \n",
              "0                     rec.autos  \n",
              "1         comp.sys.mac.hardware  \n",
              "2         comp.sys.mac.hardware  \n",
              "3                 comp.graphics  \n",
              "4                     sci.space  \n",
              "...                         ...  \n",
              "11309                   sci.med  \n",
              "11310     comp.sys.mac.hardware  \n",
              "11311  comp.sys.ibm.pc.hardware  \n",
              "11312             comp.graphics  \n",
              "11313           rec.motorcycles  \n",
              "\n",
              "[11314 rows x 3 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Put training points into a dataframe\n",
        "df_train = pd.DataFrame(newsgroups_train.data, columns=['Text'])\n",
        "df_train['Label'] = newsgroups_train.target\n",
        "# Match label to target name index\n",
        "df_train['Class Name'] = df_train['Label'].map(newsgroups_train.target_names.__getitem__)\n",
        "\n",
        "df_train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f7OHvTBaImpB"
      },
      "source": [
        "Next, sample some of the data by taking 150 data points in the training dataset and choosing a few categories. This tutorial uses the science categories."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yPxwl05BIjWX"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df_train\",\n  \"rows\": 600,\n  \"fields\": [\n    {\n      \"column\": \"index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 173,\n        \"min\": 1650,\n        \"max\": 2249,\n        \"samples\": [\n          1760,\n          2069,\n          2215\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \" Re: 80-bit keyseach machine\\nNntp-Posting-Host: top.magnus.acs.ohio-state.edu\\nOrganization: The Ohio State University\\nLines: 47\\n\\nIn article <>   writes:\\n>In article <>\\n>  writes:\\n> \\n>>Normally I'd be the last to argue with Steve . . . but shouldn't that\\n>>read \\\"3.8 years for *all* solutions\\\".  I mean, if we can imagine the\\n>>machine that does 1 trial/nanosecond, we can imagine the storage medium\\n>>that could index and archive it.\\n> \\n>   Hmmmm.  I think, with really large keyspaces like this, you need to\\n>alter the strategy discussed for DES.  Attempt decryption of several\\n>blocks, and check the disctribution of the contents.  I don't think it's\\n>at all feasible to keep 2**80 encryptions of a known plaintext block on\\n>*any* amount of tape or CD-ROM.  And certainly not 2**128 such encrypted\\n>blocks.  \\n[...]\\n\\nI don't claim to be a crypto analyist... there isn't a whole lot of good\\nliterature on the subject, and the best people don't seem to publish\\ntheir work :)  but I rather doubt the approach such folks use is brute\\nforce .  The history\\nof these things is folks find clever ways of limiting the search and\\nbang from there.\\n\\nI guess my real problem with Skipjack is I can not believe NSA would\\nmake publicly available a system they couldn't break if they wanted...\\nit just isn't in their charter.  Remember DES came from IBM, not NSA\\nand, when first published, was given a useful life of 20 years... I think\\nwe are well past that point now :(\\n\\nRemember, based on the size of the NSA budget, they spend a lot more\\non the technology of decryption than most computer companies spend on\\nR&D.  I have to imagine their stuff is real interesting...\\n\\nA friend who once worked for them  said he always enjoyed\\nmonitoring SAC's  crypto traffic :)  and I rather\\nsuspect that stuff is a bit more complex than Skipjack  \\n[BTW, folks, NSA wasn't being given the keys.  And the Walker spy case\\nshows for some of the systems, the KGB didn't need them either.]\\n\\n-- \\n Information farming at...     For addr&phone: finger             A/~~\\\\A\\n THE Ohio State University     ()____\\n      Jim Ebright             e-mail: jre+@osu.edu                 \\\\  /      \\\\\\n                          Support Privacy: Support Encryption      \\\\      \\n\",\n          \" Re: Is MSG sensitivity superstition?\\nOrganization: Netcom Online Communications Services \\nLines: 45\\n\\nIn article   writes:\\n>  writes:\\n>\\n>>Anecedotal evidence is worthless.  Even doctors who have been using a drug\\n>>or treatment for years, and who swear it is effective, are often suprised\\n>>at the results of clinical trials.  Whether or not MSG causes describable,\\n>>reportable, documentable symptoms should be pretty simple to discover.  \\n\\nBut it is quite a leap in logic to observe one situation where anecdotal\\nevidence led nowhere and therefore conclude that anecdotal evidence will\\nNEVER lead anywhere.  I'm sure somebody here can provide an example where\\nanecdotal evidence  was upheld/verified by\\nfollow-on rigorous clinical trials.\\n\\n\\n>I tend to disagree- I think anecdotal evidence, provided there is a lot of it,\\n>and it is fairly consistent, will is very important.  First, it points to the\\n>necessity of doing a study, and second, it at least says that the effects are\\n>all psychological .  As I've pointed out \\n>person's \\\"make-believe\\\" can easily be another person's reality...\\n\\nGood point.  There has been a tendency by some on this newsgroup to \\\"circle\\nthe wagons\\\" to the viewpoint that anecdotal medical evidence is worthless\\n.  But\\nevidence is evidence - it requires a \\\"jury\\\" or a process to sort it out and\\ndetermine the truth from the junk.  Medicine must continue to strive to better\\nunderstand the workings of the body/mind for the purpose of alleviating\\nillness - anecdotal evidence is just one piece of the puzzle;  it is not\\nworthless.  Rather, it can help focus limited resources in the right direction.\\n\\nJon Noring\\n\\n-- \\n\\nCharter Member --->>>  INFJ Club.\\n\\nIf you're dying to know what INFJ means, be brave, e-mail me, I'll send info.\\n=============================================================================\\n| Jon Noring          |         |                          |\\n| JKN International   | IP    : 192.100.81.100   | FRED'S GOURMET CHOCOLATE |\\n| 1312 Carlton Place  | Phone :  294-8153   | CHIPS - World's Best!    |\\n| Livermore, CA 94550 | V-Mail:  417-4101   |                          |\\n=============================================================================\\nWho are you?  Read alt.psychology.personality!  That's where the action is.\\n\",\n          \" Cold Gas tanks for Sounding Rockets\\nOrganization: Computing Lab, University of Kent at Canterbury, UK.\\nLines: 14\\nNntp-Posting-Host: eagle.ukc.ac.uk\\n\\n>Does anyone know how to size cold gas roll control thruster tanks\\n>for sounding rockets?\\n\\nWell, first you work out how much cold gas you need, then make the\\ntanks big enough.\\n\\nWorking out how much cold gas is another problem, depending on\\nvehicle configuration, flight duration, thruster Isp \\n\\nRalph Lorenz\\nUnit for Space Sciences\\nUniversity of Kent, UK\\n\"\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 11,\n        \"max\": 14,\n        \"samples\": [\n          12,\n          14,\n          11\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"sci.electronics\",\n          \"sci.space\",\n          \"sci.crypt\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "      <th>index</th>\n",
              "      <th>Text</th>\n",
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              "      <th>2</th>\n",
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              "      <td>11</td>\n",
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              "      <th>3</th>\n",
              "      <td>1653</td>\n",
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              "      <td>11</td>\n",
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              "      <th>4</th>\n",
              "      <td>1654</td>\n",
              "      <td>Re: Organized Lobbying for Cryptography\\nOrga...</td>\n",
              "      <td>11</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>597</th>\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-color: transparent;\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        document.querySelector('#df-ed8b8275-3410-4057-bfd3-9a11c1399e45 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_9b7a1fb6-7c4a-4711-81c5-6874b4529765\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "        document.querySelector('#id_9b7a1fb6-7c4a-4711-81c5-6874b4529765 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_train');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "     index                                               Text  Label  \\\n",
              "0     1650   Re: Once tapped, your code is no good any mor...     11   \n",
              "1     1651  REVISED TECHNICAL SUMMARY OF CLIPPER CHIP\\nDis...     11   \n",
              "2     1652   Re: The [secret] source of that announcement\\...     11   \n",
              "3     1653   Re: Would \"clipper\" make a good cover for oth...     11   \n",
              "4     1654   Re: Organized Lobbying for Cryptography\\nOrga...     11   \n",
              "..     ...                                                ...    ...   \n",
              "595   2245   Re: Eco-Freaks forcing Space Mining.\\nOrganiz...     14   \n",
              "596   2246  Re: Why not give $1 billion to first year-long...     14   \n",
              "597   2247  Re: PLANETS STILL: IMAGES ORBIT BY ETHER TWIST...     14   \n",
              "598   2248   Gibbons Outlines SSF Redesign Guidance\\nNews-...     14   \n",
              "599   2249   Plutonium based Nuclear Power plants.\\nOrgani...     14   \n",
              "\n",
              "    Class Name  \n",
              "0    sci.crypt  \n",
              "1    sci.crypt  \n",
              "2    sci.crypt  \n",
              "3    sci.crypt  \n",
              "4    sci.crypt  \n",
              "..         ...  \n",
              "595  sci.space  \n",
              "596  sci.space  \n",
              "597  sci.space  \n",
              "598  sci.space  \n",
              "599  sci.space  \n",
              "\n",
              "[600 rows x 4 columns]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Take a sample of each label category from df_train\n",
        "SAMPLE_SIZE = 150\n",
        "df_train = (df_train.groupby('Label', as_index = False)\n",
        "                    .apply(lambda x: x.sample(SAMPLE_SIZE))\n",
        "                    .reset_index(drop=True))\n",
        "\n",
        "# Choose categories about science\n",
        "df_train = df_train[df_train['Class Name'].str.contains('sci')]\n",
        "\n",
        "# Reset the index\n",
        "df_train = df_train.reset_index()\n",
        "df_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UjTrEnmdIo5P"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "sci.crypt          150\n",
              "sci.electronics    150\n",
              "sci.med            150\n",
              "sci.space          150\n",
              "Name: Class Name, dtype: int64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_train['Class Name'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DUgv8SOwXfAX"
      },
      "source": [
        "## Create the embeddings\n",
        "\n",
        "In this section, you will see how to generate embeddings for the different texts in the dataframe using the embeddings from the Gemini API."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "01I9uzbo4t26"
      },
      "source": [
        "### API changes to Embeddings with model embedding-001\n",
        "\n",
        "For the new embeddings model, embedding-001, there is a new task type parameter and the optional title (only valid with task_type=`RETRIEVAL_DOCUMENT`).\n",
        "\n",
        "These new parameters apply only to the newest embeddings models.The task types are:\n",
        "\n",
        "Task Type | Description\n",
        "---       | ---\n",
        "RETRIEVAL_QUERY\t| Specifies the given text is a query in a search/retrieval setting.\n",
        "RETRIEVAL_DOCUMENT | Specifies the given text is a document in a search/retrieval setting.\n",
        "SEMANTIC_SIMILARITY\t| Specifies the given text will be used for Semantic Textual Similarity (STS).\n",
        "CLASSIFICATION\t| Specifies that the embeddings will be used for classification.\n",
        "CLUSTERING\t| Specifies that the embeddings will be used for clustering."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jkS_EWfAXcxc"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "70e8bf0d283640928c2aa42fa6282dc2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/600 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from tqdm.auto import tqdm\n",
        "tqdm.pandas()\n",
        "\n",
        "from google.api_core import retry\n",
        "\n",
        "def make_embed_text_fn(model):\n",
        "\n",
        "  @retry.Retry(timeout=300.0)\n",
        "  def embed_fn(text: str) -> list[float]:\n",
        "    # Set the task_type to CLUSTERING.\n",
        "    embedding = genai.embed_content(model=model,\n",
        "                                    content=text,\n",
        "                                    task_type=\"clustering\")['embedding']\n",
        "    return np.array(embedding)\n",
        "\n",
        "  return embed_fn\n",
        "\n",
        "def create_embeddings(df):\n",
        "  model = 'models/embedding-001'\n",
        "  df['Embeddings'] = df['Text'].progress_apply(make_embed_text_fn(model))\n",
        "  return df\n",
        "\n",
        "df_train = create_embeddings(df_train)\n",
        "df_train.drop('index', axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hNTjKcD_aluG"
      },
      "source": [
        "## Dimensionality reduction\n",
        "\n",
        "The dimension of the document embedding vector is 768. In order to visualize how the embedded documents are grouped together, you will need to apply dimensionality reduction as you can only visualize the embeddings in 2D or 3D space. Contextually similar documents should be closer together in space as opposed to documents that are not as similar."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BJDHDQmeZqy2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "768"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(df_train['Embeddings'][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S5-XU-twaoK6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(600, 768)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Convert df_train['Embeddings'] Pandas series to a np.array of float32\n",
        "X = np.array(df_train['Embeddings'].to_list(), dtype=np.float32)\n",
        "X.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AV-Y7iEtbAkm"
      },
      "source": [
        "You will apply the t-Distributed Stochastic Neighbor Embedding (t-SNE) approach to perform dimensionality reduction. This technique reduces the number of dimensions, while preserving clusters (points that are close together stay close together). For the original data, the model tries to construct a distribution over which other data points are \"neighbors\" (e.g., they share a similar meaning). It then optimizes an objective function to keep a similar distribution in the visualization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FhYKF-lObC04"
      },
      "outputs": [],
      "source": [
        "tsne = TSNE(random_state=0, n_iter=1000)\n",
        "tsne_results = tsne.fit_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "31wdqnp_bH9B"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df_tsne\",\n  \"rows\": 600,\n  \"fields\": [\n    {\n      \"column\": \"TSNE1\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          13.169029235839844,\n          -31.654396057128906,\n          -10.892438888549805\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TSNE2\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          26.76736068725586,\n          7.421013832092285,\n          -20.685998916625977\n        ],\n        \"num_unique_values\": 600,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"sci.electronics\",\n          \"sci.space\",\n          \"sci.crypt\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df_tsne"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-cad0aeb8-b998-414a-a454-995bb086acf3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TSNE1</th>\n",
              "      <th>TSNE2</th>\n",
              "      <th>Class Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4.353590</td>\n",
              "      <td>17.495653</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.974765</td>\n",
              "      <td>41.019054</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10.665109</td>\n",
              "      <td>30.655214</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.912471</td>\n",
              "      <td>38.069790</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-4.066230</td>\n",
              "      <td>15.156272</td>\n",
              "      <td>sci.crypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>5.736661</td>\n",
              "      <td>-32.636852</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>8.723193</td>\n",
              "      <td>-36.857460</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>14.958293</td>\n",
              "      <td>-25.471634</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>-5.218704</td>\n",
              "      <td>-35.691990</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>-7.453671</td>\n",
              "      <td>-17.465353</td>\n",
              "      <td>sci.space</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>600 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cad0aeb8-b998-414a-a454-995bb086acf3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cad0aeb8-b998-414a-a454-995bb086acf3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cad0aeb8-b998-414a-a454-995bb086acf3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-62e1e10c-b4c4-4363-9b98-0e452829f015\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-62e1e10c-b4c4-4363-9b98-0e452829f015')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-62e1e10c-b4c4-4363-9b98-0e452829f015 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_689505c4-8647-4004-913b-6828ab35995b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_tsne')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_689505c4-8647-4004-913b-6828ab35995b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_tsne');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         TSNE1      TSNE2 Class Name\n",
              "0     4.353590  17.495653  sci.crypt\n",
              "1     3.974765  41.019054  sci.crypt\n",
              "2    10.665109  30.655214  sci.crypt\n",
              "3     0.912471  38.069790  sci.crypt\n",
              "4    -4.066230  15.156272  sci.crypt\n",
              "..         ...        ...        ...\n",
              "595   5.736661 -32.636852  sci.space\n",
              "596   8.723193 -36.857460  sci.space\n",
              "597  14.958293 -25.471634  sci.space\n",
              "598  -5.218704 -35.691990  sci.space\n",
              "599  -7.453671 -17.465353  sci.space\n",
              "\n",
              "[600 rows x 3 columns]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_tsne = pd.DataFrame(tsne_results, columns=['TSNE1', 'TSNE2'])\n",
        "df_tsne['Class Name'] = df_train['Class Name'] # Add labels column from df_train to df_tsne\n",
        "df_tsne"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pTj8HfhpbJ9X"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(8,6)) # Set figsize\n",
        "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
        "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Class Name', palette='Set2')\n",
        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
        "plt.title('Scatter plot of news using t-SNE')\n",
        "plt.xlabel('TSNE1')\n",
        "plt.ylabel('TSNE2');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8JQbX4pcMdBe"
      },
      "source": [
        "## Outlier detection\n",
        "\n",
        "To determine which points are anomalous, you will determine which points are inliers and outliers. Start by finding the centroid, or location that represents the center of the cluster, and use the distance to determine the points that are outliers.\n",
        "\n",
        "Start by getting the centroid of each category."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nUIkLxtMK4qC"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"centroids\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"TSNE1\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          14.79300308227539,\n          3.0627570152282715,\n          3.694504499435425\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TSNE2\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"samples\": [\n          -0.5331496596336365,\n          -27.739778518676758,\n          27.894010543823242\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "centroids"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-eaba8f44-0c24-4160-b878-732d05acdd39\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TSNE1</th>\n",
              "      <th>TSNE2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sci.crypt</th>\n",
              "      <td>3.694504</td>\n",
              "      <td>27.894011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sci.electronics</th>\n",
              "      <td>14.793003</td>\n",
              "      <td>-0.533150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sci.med</th>\n",
              "      <td>-23.307659</td>\n",
              "      <td>2.060012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sci.space</th>\n",
              "      <td>3.062757</td>\n",
              "      <td>-27.739779</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eaba8f44-0c24-4160-b878-732d05acdd39')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-eaba8f44-0c24-4160-b878-732d05acdd39 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-eaba8f44-0c24-4160-b878-732d05acdd39');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3313c489-00dd-4f49-969f-5cd44732e11a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3313c489-00dd-4f49-969f-5cd44732e11a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3313c489-00dd-4f49-969f-5cd44732e11a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_ee7e7414-03b5-4251-afe8-cf51d81749a2\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('centroids')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_ee7e7414-03b5-4251-afe8-cf51d81749a2 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('centroids');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                     TSNE1      TSNE2\n",
              "Class Name                           \n",
              "sci.crypt         3.694504  27.894011\n",
              "sci.electronics  14.793003  -0.533150\n",
              "sci.med         -23.307659   2.060012\n",
              "sci.space         3.062757 -27.739779"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def get_centroids(df_tsne):\n",
        "  # Get the centroid of each cluster\n",
        "  centroids = df_tsne.groupby('Class Name').mean()\n",
        "  return centroids\n",
        "\n",
        "centroids = get_centroids(df_tsne)\n",
        "centroids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GJH4Oo6E-r_6"
      },
      "outputs": [],
      "source": [
        "def get_embedding_centroids(df):\n",
        "  emb_centroids = dict()\n",
        "  grouped = df.groupby('Class Name')\n",
        "  for c in grouped.groups:\n",
        "    sub_df = grouped.get_group(c)\n",
        "    # Get the centroid value of dimension 768\n",
        "    emb_centroids[c] = np.mean(sub_df['Embeddings'], axis=0)\n",
        "\n",
        "  return emb_centroids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1tas9Yg4_iyq"
      },
      "outputs": [],
      "source": [
        "emb_c = get_embedding_centroids(df_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aMvdYLjKl32a"
      },
      "source": [
        "Plot each centroid you have found against the rest of the points."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jpN02WY3Ogji"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the centroids against the cluster\n",
        "fig, ax = plt.subplots(figsize=(8,6)) # Set figsize\n",
        "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
        "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Class Name', palette='Set2');\n",
        "sns.scatterplot(data=centroids, x='TSNE1', y='TSNE2', color=\"black\", marker='X', s=100, label='Centroids')\n",
        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
        "plt.title('Scatter plot of news using t-SNE with centroids')\n",
        "plt.xlabel('TSNE1')\n",
        "plt.ylabel('TSNE2');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "onFfUf1XoEQW"
      },
      "source": [
        "Choose a radius. Anything beyond this bound from the centroid of that category is considered an outlier."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "87cDfNpvOu7f"
      },
      "outputs": [],
      "source": [
        "def calculate_euclidean_distance(p1, p2):\n",
        "  return np.sqrt(np.sum(np.square(p1 - p2)))\n",
        "\n",
        "def detect_outlier(df, emb_centroids, radius):\n",
        "  for idx, row in df.iterrows():\n",
        "    class_name = row['Class Name'] # Get class name of row\n",
        "    # Compare centroid distances\n",
        "    dist = calculate_euclidean_distance(row['Embeddings'],\n",
        "                                        emb_centroids[class_name])\n",
        "    df.at[idx, 'Outlier'] = dist > radius\n",
        "\n",
        "  return len(df[df['Outlier'] == True])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CsVsod5MKd3X"
      },
      "outputs": [],
      "source": [
        "range_ = np.arange(0.3, 0.75, 0.02).round(decimals=2).tolist()\n",
        "num_outliers = []\n",
        "for i in range_:\n",
        "  num_outliers.append(detect_outlier(df_train, emb_c, i))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vReUSOjbNHQv"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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Y5THuvPPOM/6F/XSzZs3i0KFDdOvWjWbNmgXddvvtt1O5cmXmzZvHjh07cn2foWTM3z333EN8fHzgepfLxdChQ7Esi6+//vqcHiM/ffrppwD897//zbIXxbXXXkvdunWZPHlyyJ8dNmwYxYoVC3lbkSJFslxnWVbQcpGdCy64gFatWrFq1So2bNgQdNuECROAv5fBnB6zdOnSIfeWy62M5aZHjx40aNAg6Lb77rvvrE+eHaoxOjr6rPYAKFmyZMhznVxwwQV06dKFTZs2hVyOixYtyvDhw4P22rv44otp0qQJf/zxB8eOHQtcn3Go4LBhw6hQoULIx8pwLstQZmlpaUyaNIlixYrx0EMPBe0FVKNGDfr374/b7Q4sA/kht9uqSZMm4Xa7ufnmm7Oc7+rBBx+kePHiTJw4MdvD4E7XpEkTrr322qDrrrvuOlwuF8uXLz/r+ciP5SonQ4YMybLcnct29Z577glatkqVKkX79u0De1dlfp6jo6Pp1q0bbrc728NIM9u/fz/fffcd8fHxge1uZsWLFw+suwcPHmTSpEnUr1+ff/zjH0HTxcTE8M9//hO/3x9yGc7v1xFCv7ctXryYTZs20bhx4yzvx926daNp06b8+eefLFq06Iz3PXny5MByXKVKlcD1lmXx6KOPntO2UkQkJzqcTUSkgAwdOpR+/frx0ksv8dVXX9nyGKEOL8o4wWqoQ8kyPvjv2rUr5P2FOuSradOmOJ1O1qxZE7hu6dKlQPpgVKhzDWWc5+GPP/7IMsiUm932M1u9ejUALVu2zHKby+WiefPmbN++ndWrV5/TCXHP9DgXXnghF1xwAdu2bePo0aNh8e1dS5cuJSoqiunTpzN9+vQst7vdbg4cOMDBgweJjY0NXB8TExPyUK6LL76YunXrMmXKFLZv306HDh1o2rQp9evXP6tD+K655hrmzZvH+PHjefTRR4H0QYapU6cSFxdHmzZtAtN269aNTz75hHvvvZfOnTtz2WWX0aRJE6pVq3Y2T0VIGa9n8+bNs9xWsmRJ6taty4IFC3K8nxYtWlChQgXeffddVq1aRZs2bWjSpAl169bN1aGYp1u0aBGffPIJS5cuZf/+/VnO+7R79+4sy3H16tVDDipkDAgdOXKE4sWLA+nLhcPh4PLLL8+xJa/L0On+/PNPTpw4QZMmTShTpkyW21u2bMnbb78dtA05V7ndVp1pvS5dujSXXHIJv//+O5s2baJOnTo5Pm6obW5UVBRxcXFndQ6p/F6uziTUNvdctqunD8rCub3vZLZixQp8Ph/NmzfPdqA787RerxeHwxHyfcjj8QCEPEdffr2OmZ3peb700ktD/kzLli1ZtGgRq1evDrmtOv1+Qk1TtWpVKlasyPbt2/OSLSKSIw0iiYgUkMaNG9O5c2e+//57vvvuO7p165bvjxFqMCPjF5Az3Zbx4fp0GeeZyMzlchEbG8v+/fsD1x06dAggx8Gx48eP5+oxziTjxLeZ9w7KLOP6UCfIze/H2bFjB0eOHAmLQaRDhw7h8XgCJ33NzvHjx4MGAOLi4oL2FMngdDr5+OOPGTlyJN9//z0vv/wykP6X/2uuuYaHHnooMFBxJldeeSUlSpRg0qRJPPzwwzidTubMmcOhQ4e49dZbcbn+/ijSsGFDPv/8c9555x2+//57Jk6cCKQP2g0ePDhw7qS8yHg9s1vecrsclihRgq+++oo33niD2bNnk5ycDEBsbCw33ngj99xzT67P2zRz5kzuv/9+YmJiuOyyy6hWrRpFixbFsqzAuW1C7RGT3fl6Mp5Lr9cbuO7o0aOULl065B4up8vrMnS63K6jef3lPJTcbqvyu+1Mr4XP58vVfUD+Llc5CTXv57JdDTWgmbEs5uV9J7OM1yHUXnSny3gfWrFiBStWrMh2usx76mXIr9cxs1DLZMbzl9235+X2/Ss32zMNIomIXTSIJCJSgB5++GFmz54dONFoKBm7oWf3AfvIkSO5Punrudq3b1+Wvzp7PB4OHjwY9ItDxi8KEydOzNVf7zMLNYBxJhmPtXfv3pC3Z1x/rgM7GT+/b9++kHvChHoch8NxxtfNTiVKlMDv9+dqb5rMzvT8ly5dmn/961/861//4q+//mLBggWMHTuWzz77jCNHjvC///0vx/svUqQIXbt25euvv2bevHlcccUVjB8/HiDLCaAhfbA149vvVq5cydy5c/nss894+OGHKVu2bNBJoM9G5tczlOyuD+WCCy7gueeew+/3s3HjRn777Tc+//xzRo4cic/n44EHHsjV/bz++utERUXx7bffZjm06oknnjjr1zKUkiVLcujQIVJTU3McSMrrMhTqMSH75zS/1tHMznZbtW/fPmrVqlUgbbmVX8tVTkKt8wW1XT1bGe91ufnGsYy2AQMGMHz4cFu7cuNcnuecDl/MaTk+m+2ZiMjZ0gGzIiIFqHr16txwww1s27Yt6GvNM8v40BxqV/+//vrrnPewORuhfplctGgRXq836BvEEhMTA7fZLeNxQ7V5PJ7AOacuueSSfHmcUF+r/Ndff7Fr1y6qVKkSNKBXunTpkK+b1+sNed6pjL/IZ95zJK8aNWrE4cOHs5x7KL9Ur16dPn368Nlnn1GsWDFmzZqV65/NGCyaMGECBw4cYO7cudSuXfuM30IXHR1NkyZNGDJkCI899hjAWT3m6TKWh99//z3LbUePHs3ToVUOh4NatWrRv39/Ro8enaUxp9f3r7/+4uKLL84ygOTz+fJtXWrUqBF+v5+5c+fmatr8WIYuvPBCihYtytq1a0MOnmasU+e6jmaW223VmdbrI0eOsGbNGmJiYrK8Jvkh4w8EOe3ZktNyZYeC2q6erYYNG2JZFr///nvIPVlDTWvneQch969jKGd6nuHv5TLUYYCZnWl7tnXrVnbu3HnWbSIiuaVBJBGRAnbvvfdSqlQp3nnnnZC71desWZMSJUowa9asoMMwUlNTeeaZZwoylbfffpvDhw8H/n3y5ElGjBgBpJ90NMO1115LqVKleOutt0KehNTn84X8pS0vOnbsSJkyZZg6dWrgXEwZPv74Y7Zt28Zll112TudDgr/n7+233+bAgQOB671eLy+++CI+n4/evXsH/UyDBg3YsWNH4FCUDG+//XbIQwtKlSqFw+HIlw/8AwYMAODxxx8P+Vf748ePZ3m+zmTr1q1s3bo1y/WHDx/G7Xbn6vCoDE2bNqVGjRrMmjWLL7/8ErfbneUktpB+0tnU1NQs12esB5kf88SJE/zxxx+5PoF6hw4dKF26NFOmTMlyqMubb76Z68HZDRs2hPwrf8Z1mRszzgeU3etbuXJlNm/eHPR6+f1+3nzzzZAnoc+Lm2++GYAXXngh5HKR+br8Woaio6O56qqrOHbsGK+//nrQbVu2bOHTTz8lKioqcNL//JDbbVXPnj2Jioris88+46+//gq6j9dff52UlBR69ux5Vuf9yq2M5SHUMns2y5UdCmq7erbKli1Lt27d2Lt3b2C7m9mxY8cC625cXBxXXXUVK1euZOTIkSEHb7ds2RJyu3Y2zvQ65qRp06ZceOGFLFq0KMt5x6ZPn87ChQupUaMGTZs2PeP9XHXVVYHleNu2bYHrfT4fL730Up4PwRMRyQ0dziYiUsDKlCnDXXfdle2hQFFRUdxyyy383//9H1dffTVXXnklHo+HX375hfLly2d7LgU71KxZk+7du9OlSxdcLhezZs1iy5YttG3bNugXwNjYWN544w3uvfderr/+elq1asXFF1+Mw+Fg165dLFmyhEOHDp3xPBW5Vbx4cZ599lkeeOABbr75Zrp06UKlSpVYtWoVycnJxMfH8/TTT5/z4zRp0oQ777yT999/nx49etC5c2eKFi3K3LlzWb9+PU2bNuWOO+4I+pk77riD5ORkBg0aRLdu3ShdujRLlixh27ZttGjRIstfn4sXL05iYiILFy7k4Ycf5sILL8SyLNq3b3/WhwW2atWKhx9+mBEjRtC5c2euuOIKqlSpwvHjx9mxYwe///47TZo04YMPPsjV/a1bt47BgwfToEEDLrroIsqXL8+BAweYNWsWbrc7y7cf5aRXr168/vrrvP3227hcLq666qos07z//vv89ttvNGvWjCpVqlCsWDE2btzIzz//TOnSpenbt29g2uXLl3PLLbfQokWLwLeKnUnx4sV5+umnefDBB7npppvo1q0b8fHxLFq0iA0bNtC8efOQf9U/3bx58/jf//5Ho0aNqFGjBnFxcezatYtZs2ZhWVbQMnHhhRdSoUIFpk6disvlolKlSjgcDnr16kXlypUZMGAATz75JNdccw2dOnXC5XKxePFi/vjjD9q1a8ePP/6Yy2c3e0lJSdxzzz28/fbbdO3alY4dO1KxYkX27dvHokWLaNSoES+88AKQv8vQww8/zMKFC/nss89YsWIFl156KQcPHmTatGkcO3aMxx9//Ky+lTEnud1WValSheHDh/P0009zzTXX0LVrV8qWLcvvv//OkiVLqFmzJo888ki+dWXWsmVLLMtixIgRbNiwIbAX46BBg85qubJDQW1X8+KJJ55gw4YNjBkzhgULFpCUlERUVBTbtm0jOTmZt99+O3Ci6ieeeIK//vqLN954g0mTJtGkSRPKlSvHnj17+OOPP1ixYgUjRow4p2XvTK9jThwOBy+++CK33XYbDz74IFOmTKFmzZr8+eef/PDDDxQvXpyXXnopx29Xq1KlCg8//DAvvPBCYDkuWbIkycnJHD16lNq1a7Nu3bo8z6OIyJloEElEpBDccsstfPHFF9me+PL++++naNGifPXVV3z11VeUK1eObt26cd9999G9e/cC63z99dcZOXIkkydPZs+ePVSoUIH77ruPgQMHZjnfQ6tWrZg0aRIffvghycnJLFy4kKioKMqXL0/Lli3p3LlzvnV17NiRL774glGjRpGcnExKSgrlypWjX79+DBo0KFcnYc2Nf/7zn1xyySV89tlnTJgwAY/HQ7Vq1XjggQe4/fbbs+yt0KpVK0aOHMnIkSOZOnUqxYoV47LLLuPVV18N+W1BAC+99BLPP/88ycnJTJ06Fb/fzwUXXHDWg0gAAwcOpEmTJnz66acsWrSI2bNnU6JECSpUqMD1119/Viemrl+/PgMHDmTBggXMnTuXw4cPU7ZsWerVq0f//v2DvlUtN66++mrefPNN3G437dq1Iy4uLss0N954I6VLl2bZsmWBQ5EqVKjAjTfeyG233UblypXP6jFP16VLF0qWLMlbb73FtGnTiI6OplmzZowZM4b33nsvV4NIl19+OTt37uT3339n1qxZpKSkUL58eVq3bs2AAQNo0qRJYFqn08lbb73FK6+8wvTp0zl27Bh+v5+mTZtSuXJl+vXrR3R0NB9//DETJkwgJiaGZs2a8fzzzzNjxox8GUQCeOCBB2jcuDGffPIJc+bM4fjx48TFxVG/fv0sewPl1zJUpkwZxo4dy6hRo5g5cyajR4+mSJEiNGzYkDvuuIOkpKR8mbcMZ7Otuummm6hevToffvghM2bM4MSJE1SsWJE77riDu+++27Zzzl100UW88MILfPjhh3zxxRecPHkSSB98OJvlyi4FtV09W6VLl2bMmDF8/PHHfPfdd3z11VdYlkXFihW57rrrgr7xs0SJEnz66ad89dVXTJkyhRkzZnDy5EnKlStH9erVGT58eJ7Pq5bhTK9jbiQmJvLNN9/w9ttv8+uvv/Ljjz8SGxtL9+7dGTRoEDVr1szV/dx2223Ex8fzwQcfMH78eIoXL05SUhL//Oc/bRsIFREBcPj9fn9hR4iIiIiInK3+/fuzYMEC7XUhIiJSQHROJBERERERERERyZEGkUREREREREREJEcaRBIRERERERERkRzpnEgiIiIiIiIiIpIj7YkkIiIiIiIiIiI50iCSiIiIiIiIiIjkSINIIiIiIiIiIiKSIw0iiYiIiIiIiIhIjlyFHWCa/fuPcr6fitzv95OamkqRIkVwOByFnXNGarWHSa1gVq9a7WFSK5jVq1Z7mNQKZvWq1R4mtYJZvWq1h0mtYFavWs3kcEBcXMkcp9Mg0lny+9Egkh+cTpcRz4Na7WFSK5jVq1Z7mNQKZvWq1R4mtYJZvWq1h0mtYFavWu1hUiuY1avWyKbD2eSseTwepkz5Bo/HU9gpOVKrPUxqBbN61WoPk1rBrF612sOkVjCrV632MKkVzOpVqz1MagWzetUa2Rx+v8bczsa+fTqcze/34/F4cLlcYb/Ln1rtYVIrmNWrVnuY1Apm9arVHia1glm9arWHSa1gVq9a7WFSK5jVq1YzORxQrlzOh7NpTyTJE4/HXdgJuaZWe5jUCmb1qtUeJrWCWb1qtYdJrWBWr1rtYVIrmNWrVnuY1Apm9ao1cmkQSc6ax+Nh+vSJRuzyp1Z7mNQKZvWq1R4mtYJZvWq1h0mtYFavWu1hUiuY1atWe5jUCmb1qjWy6XC2s6TD2UREREREREQkkuhwNrGN3+/jyJHD+P2+wk7JkVrtYVIrmNWrVnuY1Apm9arVHia1glm9arWHSa1gVq9a7WFSK5jVq9bIpkEkOWsej5effpqBx+Mt7JQcqdUeJrWCWb1qtYdJrWBWr1rtYVIrmNWrVnuY1Apm9arVHia1glm9ao1sOpztLOlwNhERERERERGJJDqcTWzj8/nYv38fPl/47/KnVnuY1Apm9arVHia1glm9arWHSa1gVq9a7WFSK5jVq1Z7mNQKZvWqNbJpEEnOmtfrZcGCZLze8N/lT632MKkVzOpVqz1MagWzetVqD5NawaxetdrDpFYwq1et9jCpFczqVWtk0+FsZ0mHs4mIiIiIiIhIJNHhbGIbn8/H7t07jdjlT632MKkVzOpVqz1MagWzetVqD5NawaxetdrDpFYwq1et9jCpFczqVWtk0yCSnDWfz8uKFYvx+cJ/lz+12sOkVjCrV632MKkVzOpVqz1MagWzetVqD5NawaxetdrDpFYwq1etkU2Hs50lHc4mIiIiIiIiIpEkt4ezuQqgRcLcunVr+fDDUSxfvoy0tJNUqlSZnj2vpU+ffoFpVqxYxv/93xusX7+W4sWL06JFSx544FFKlCgRdF9paWm8//47fP/9dxw9epSLLrqYgQPvoXnzlmoN49a89BYpUpSOHTtx112DKVasWIH2mvTcqtW+ZfZs+Hw+du7cRsWKVbCs8N8J16RetdrDpFYwq1et9jCpFczqVas9TGoFs3rVGtn0LJ3nFiz4jbvvvo2DBw8yYMAdDBnyCJdddjl79+4OTLNhwzqGDBnEyZOp3Hffg3Tr1pMffpjBE08My3J/zz77H8aO/ZxOnboyZMjDOJ1OHnlkCMuWLVVrmLbmpXfQoCE0atSISZMm8Pjjem7VWvDL7Nny+Xxs2LDWmOPdTepVqz1MagWzetVqD5NawaxetdrDpFYwq1etkU2Hs52lSDqc7dixFG644Trq12/IM8+8mO3I6yOP3M+GDev54otvKF48fW+DyZMn8OKLzzBixFu0aJG+Z8Hq1SsZOHAAgwYN4cYb+wNw8uRJbrmlL7GxZXnnnQ/VGmatpvWqVa0iIiIiIpL/9O1skqOZM6dz4MB+Bg4chGVZnDhxIssI7LFjKfz++3w6d+4a+KXR5/NSt25dihYtxuzZMwPTzpkzC6fTSa9e1wSui4mJoUePXqxcuZzdu3epNcxa89rr83nZvPkPOnXqoudWrQW+zC5evJCkpGYh/1u5ckVgOo/Hw4cfvkufPr1o164V11zTjdGj38Pj8QTd37PP/ifb+0tKasbevXvOqTcvMtYxE07yqFZ7mNQKZvWq1R4mtYJZvWq1h0mtYFavWiObzol0Hlu4cAHFixdn7949DB/+MFu3bqFo0aJ07tyN++57iJiYGP74YyNer5fatS8J/JzP52f37h1cfHEt1q9fF7h+/fp1VK1aLfALZoa6desBsGHDeipUuECtYdSa116fz8/27VuoUqU6tWol6LlVa4Eusxl69+5H3bqXBF1XpUrVwOWnn36cH3/8ge7de1KrVm1+/nkWH3wwij179jB06GOB6Xr1upZmzVoE3Y/f7+fll5/nggsqER9f/pxbz1bmdSzcD89Xqz1MagWzetVqD5NawaxetdrDpFYwq1etkU2DSOexrVu34vV6GT78YXr06MXddw9myZJFfPPNWI4ePcpTTz3H/v37AChXrlzg51wuF61bt2P69O9ZvnxJ4Pr9+/cRF1cuy+NkXLdv3161hllrXnszWjM69NyqtSCX2QyJiY1o165jyNvWrFnF7NkzGTDgTu68824Arrvuet566zXGjv2c6667nosvrgVA/foNqV+/YdDPL1u2lNTUVDp16pIvrWcr8zoW7tRqD5NawaxetdrDpFYwq1et9jCpFczqVWtk0yDSeezEieOkpqZy9dXX8cAD/wSgTZv2uN1uJk4cx5133s3JkycBiIqKCvyc1+tl06YNREVFBW6H9HOeZJ4uQ3R0NABpaSez3KbWwm3Na29Ga82atYiOjtZzq9YCXWYzO378GNHRMbhcwW9ny5alD2p16NApqLVdu46MGfMZs2bNCAwihTJz5nQcDgdXXlk4g0iZ1zGn01koDbmlVnuY1Apm9arVHia1glm9arWHSa1gVq9aI1tY7rC1atUq7r77blq0aEFiYiI9evTgk08+CZpm8eLF3HDDDSQmJtK6dWueeeYZjh07luW+0tLS+N///kdSUhINGzakT58+zJs3r6BmJazFxMQA0LFj56DrM35pWrlyeWAat9sduN3v93PgwD7S0tICt2fcX+bpMqSlpQEQHR2T5Ta1Fm5rXnszWv1+v55btRb4MpvhueeeplOnNnTo0Jr77ruLtWtXB27LePyMrozWjH+vW7c22/v1eDz8+ONM6tdvSMWKlc6pMa/nb+rX7xrGjv0iy/O4adMf/PvfQ+nTpxcdOrSme/cO3HvvP0hO/vmcOs9F5u1BuFOrfUzqVas9TGoFs3rVag+TWsGsXrVGtrAbREpOTqZv374cOHCAQYMG8dhjj9G2bVt27fr7RKxr1qxhwIABpKamMmzYMHr37s3YsWMZMmRIlvsbNmwYH330EVdddRWPPfYYTqeTgQMHsnDhwoKcrbBUrlw8AGXLlg26PjY2/d9Hjx7NdPjJvsDtLpeLSy9N4sCB/cTFxQeuj4srFzjkJbO/D4OJz3KbWgu3Na+9Ga0ul+vUoUt6btVacMtsVFQUbdu2Z8iQh3nhhVe488572LRpI4MG/YP169MHh6pVqw7AihXLglpXrlx+aj6yP1n2/Pm/cvjwYTp16npOnZn17t2Pxx9/Oui/08/fNHr0ezRt2owhQx6mUaMmTJ/+Ha+//krQ/ezevZPjx4/TtWt3hgx5hFtvvROAYcMeYuLEcfnWezYybw/CnVrtY1KvWu1hUiuY1atWe5jUCmb1qjWyhdUgUkpKCkOHDqVt27aMGTOGAQMGcP311/PII4/w6KOPBqYbMWIEpUqV4tNPP+WGG27gwQcf5IknnmDu3LkkJycHplu+fDlTp07loYceYujQofTt25ePP/6YSpUq8fLLLxfGLIaV2rXrArB3b/D5STLOV1KmTCw1a16M0+lk3bq//8Lv9XpZsWIJGzasp1athMD1tWrVZuvWLRw7lhJ0f6tXrzx1ewJ5pVZ7WvPa6/V6WbNmBampqXpu1Vrgy2yDBok888xL9OjRi6SkNvTvP4BRoz7C4YB33hkJQMuWrbnggoqMHPk6P/00m+3bt/HJJx/w3nv/h9PpDDr87nQzZ07H5XLRvn3o8y3lRWJiIzp37hb0X5kyZYC/z9906613MGzY41x9dW+GDXucTp06M2XKBDZu3BC4n1atkhgx4k1uv30gPXtew/XX38Abb7zDxRcnMHbs5/nWezYytgdeb/h/q4la7WNSr1rtYVIrmNWrVnuY1Apm9ao1soXVINLkyZPZt28fDz74IJZlcfz48SxfM52SksIvv/xCz549KVHi72/+6dWrF8WKFWPatGmB66ZPn47T6aRv376B62JiYujduzdLlixh586d9s9UGMv4BWnKlIlB10+ePAGn00mTJk0pUaIEzZpdyvffT+P48YzDBf38/PMcTpw4HnRS27ZtO+D1epk4cXzgurS0NL77bjKXXFL/nL6NSa32tOa918+JE8eZMeM7PbdqLfBlNpQqVaqSlNSGJUsW4vV6iYmJ4aWXXqNUqdI89tij9O17NR9++B633HI7pUqVpmjRYiHv5/jx4yQn/0SLFq0oXbpMvjYeP34Mj8eT5frTz9+Uzk/Dhon4/X5mzZpxxvt1Op2UL1+BlJSUM05nn/TtAZiwG7ha7WNSr1rtYVIrmNWrVnuY1Apm9ao1koXVPlu//vorJUqUYPfu3QwaNIjNmzdTrFgxevbsyb/+9S9iYmJYt24dHo+H+vXrB/1sdHQ0devWZc2aNYHr1qxZQ40aNYIGmwAaNmwYuL1ixYpn1ej1erAsV2Ck0ul04vV6AAdOpxOPx4PD8fdly3JgWRmXLSzLwuNxY1lOLMvC7XbjdP592eVy4nBkXE5/eTweT9DlqKgo/H4fHo+XqKgofD4fXu/fl30+Ly5XxmUfLpcLn8+Lz+cnOjoKv9+P3+/nkksuoUePnkyZMgmfz0ujRk1YsmQRs2f/QP/+A4iPj8fptBg48B7uuecOBg++i549r2bv3j2MG/cNLVq05LLLLjs1Tx4aNKhPhw5XMmrUWxw+fICqVasxdepkdu7cwWOPPQH4Aicr83q9OBwWPp8/23mC9F6fz0dCQgJXXdWLyZMn4vV6aNKkGYsXL2T27B+49dbbiI+Px+/3c8899zJw4G0MHjyQq6++jt27d/Htt99w6aUtadWq1anXzKJevXq0b9+RUaNGcujQAapUqcq0aVPZuXMHw4c/jstlBb1maWluwHHG18nv9xEV5aJu3bp0734VU6dOxufzkpjYmGXLljBr1kz69x/ABRdUwOfzMXDg3dxzz50MHpy+R8G+fXv59tv05zUpKQmv14ff7ycxseGp1rc4fPgglStXYdq0KezcuZPhw/+Nw5E+Tx6PF4fDgcPhIC3NfcZlz+Vy4vN5sSyLSy65JEvv0qWLA8tBhQrpX28e3Hs1+/bt44svPqNFi0tJSkoKLG9n7n0c8ONyOfF601/j9OfRk+365HRaREW58Hi81KlTJ7Ac+HxeGjduyqJFv/Pjj7O49dbbKFcuDssiy3Kwa9fOwHLQsmVLnE4HAPXqXRJYZg8d2k+1atWDllnLItM6lL58WpYzsD65XOnbAr//78uWlX64Ve3adQLrl9froXHjpixduvjUcnAbF1xQAY/Hy913D+Kuu25n8OCB9Op1DXv3Bi8HGa9fYmJD2rXrEHheK1WqzPTp37Fz5w6GDXssME9erzfQkpbmOeM2IuPNMtS2YPHiRfz44w/0738b8fHlcDot7rprEHfffXvIbUGrVpcFlsPcbAv+XibTt5l+v+Oct3sVKlTA7XaTkpJC6dKlqVGjBqNHf87WrVs4fPgQ1avXoHjx4owc+TqJiY0Dz1fmZe+nn2aTmppKhw6d8Hq957wtz7j/5557mhMnjuN0OmnYsBF33XUv9es3xO/3kZqaCqQvN263m6ioKBwOi/r1GwGwbt2awPORsey53W6OHz9OSspRfvttHvPn/0K7dh1CzlNBvD81btzi1PpKnt+fQq1P4MfpzL/3XJ/PT6NGzQvsPffc5slFw4ZNcTgcgZZw+BxxpnlKTGwW9HkpXD8b+f3QpMml+HzewP3YvezldZ4sy6JBgyY4na6wW59CzVOTJpfidruxLH+YrU+h56lJk0vxeNz4fL6wW58yzxNAYmKzsFyfQs1TYmKzwPllwml9On2efD7/qW2BD4/HHXbr0+nz5PP5ady4BUDg80I4rU+Z58npdNGgQRMcDivQGy7rU6h5St8WeE79fhRe61NBL3u5EVZ7Im3evBmv18ugQYO4/PLLefPNN7nuuusYM2YMw4cPB/4+3KJ8+fJZfj4+Pp49e/4+z8XevXuJj8967o2M6zJPm1srViwFYNWqZaxalX6ujWXLFrF+ffohHosW/camTemHHsyfP5ctWzYDkJw8m507twEwZ84M9u7dDcAPP0zl4MEDAEyfPoGjR48CMGXKN6SmnsDj8TBlyjd4PB5SU08wZco3QPo5SqZPnwDAwYMH+OGHqafmeTdz5qT/1Xrnzm0kJ88GYMuWzSxfvpDSZYpRrlxJ4uNLERtbnBdeeI777ruPtWtX88YbI/jjjw0MHz6cf/97OOXKlSQ2tjhJSS0YPXo0xYsX5c03X2Xy5An07t2bt98eSVxc+jTx8aWJiyvJq6++wq233sqMGdMZMeJ/gJ9Ro0bRvv0VxMeXpmzZEpQtW4L4+NKUiS2O230y5DwdPXqY2LIliI0tTlxcSeLjS/P8889y3333sW7dGl5//ZVA67/+NSwwT61aNTvVWozXX3+FyZMn0KdPb/7v/0ZSrlypwDzFx5fitddGcOuttzBz5nRee+1lwMeoUaPo2LFN0DzFxhYntmwJjh49nO3r5Pf7iC1bPDB/L774fOB5ffPNV9m4cX3gec2Yp6SkSwOtb775KpMmjadPn/TnNTa2eNDrlN56KzNmTDvV6mfUqHfo2LFt0DyVK1eS0mWKsXz5wmyXvUOH9lO6TLGg+Tu9N/NykDFPwb2vZer9v6DX6cy9bQLzVK5cScqVK0XpMsXYuHFtyPVp1aqlgeUgPr4UcXElA8vB2rWref31V9i0aWNgOciYp9OXgylTJgaWg9OXw4xldubM77Mss5nnKS6uJLFlS2BZDrZs2cz8+XMB2LRpA4sW/QbAX39tJLZsycD8Zaxf69at4Y03RmRaDoYF5umyy5oHWt94I+tykPl1ev31VwPPa/q5ctKX2SuvbBc0T7GxxSkTW4K5c2dmu42wLEegIdS2YNOmDYHWcuXSp2ndunm224LMy2FutgWZtx1lYtOf13Pd7u3YsZ2oqGgWLfo1sN1bsCCZmjUvonjxoixc+AsLFy7A5/MFBkdP35ZPnPgtRYsWo1ixmHzZls+fP5fLL2/D4MEP0KtXL26/fSB//LGRwYMHsn79Wo4ePcq+fbtOTftLYJ52797Jl19+cmpbviNoWz5//lzeeutVevXqzE039WbkyNdp1KgxHTteGXKe7H5/OnnyJEuWLDin96dQ69P69atZtmxRvs/TwoW/4vV6bX/PPdd58nq9/PDDVDZv3pQvr5Pd8+T1evnpp5msXLk0X14nu+dp+fLFbN68qUCXvbzM06FDh5g6dRxerzcs16fM83To0CGWL18clutTqHnavn0Ly5cvDsv16fR5Wrp0IXPnzsLr9Ybl+pR5nrxeL/Pm/cjChb/my+tk9zwtX76Y3bt3ht36lN08LVu2kGPHUsJufTp9nrxeL9OmTWD//r358jrZOU9//LGO5csX89tvP4fd+mT363T6PGV05cThD6PTkHfs2JGtW7fSr18/nnrqqcD1TzzxBGPHjuX7779n6dKlDB06lK+//jqwR1GGRx99lNmzZwdOmt2xY0cuvPBC3nvvvaDptm7dSseOHRk+fDgDBgw4q8bduw8auyeSZVnEx5diyJglbNxTWIc9pLu4fAle79eYAwdSSE1NyzJPlgVxcSXDqnX//qP4fKFH0l0ui7JlSxR6b0br3r1HTv1VLeuyFx0dFXbP7b59R/F4vFnWJ/CH3TJ78OAx0tLcIf864HBAuXKF/9z+vRwcBqyQ24giRWKIjS0eNq0HDx7jxImTudruZexplHme1q9fy913386ll17Gs8++lOWvOGlpaSxdupB33hnJ/v37+eyzsZQsWTpoW75//16uvbYHHTt2ZvjwJ2zblm/btpUBA24gMbEJr7zyOsePH+fWW28gLe0kQ4b8k3r16rN8+VJeeeUFUlNTueCCinz++TdB87R9+zZ27drJvn17+emn2TidLh566J/Ex1co8Pcnh8PBqlXLqF27HkWKFAnrv7adPJnK2rWrqF+/0anHD9+/IAKsWLGEevUaEhUVHRafI840T+BgxYolXHJJQ6Kjo8P6s5Hb7WbdulXUrVsfh8MKm70MQs2Tx+Nm5cqlp/6i7wir9en0eXI4YPXqFSQkXBL4JsxwWZ9CzZPf72fNmhXUrn0JUVHRYbU+nT5PaWknWbNmBfXrN8bv94fV+nT6PDkcDlauXEKdOg2IiYkJq/Xp9Hlyu9NYt241des2wOEgrNanUPN08uRJ1q1bRb166Ye7h9P6dPo8+f3+U+9hiYE9rcNlfTp9nnw+H2vXrqR27XpERbnCan0q6GXP43FTsWLwF+2EElaHsxUpUgSAHj16BF1/1VVXMXbsWJYuXRqYJuOrojM7efJk4PaM+8tuusyPdzacThd+/9+7lmZclyFjgTnz5ajA5aiovF12OCyiotJ3JMtYGM582YnLlX55454UVu04kvuZtpHD4ch2niC8WtMPhfBl83qkf+APl16Xy4nH4zh1OXjZC8fn1um0yBjODl6f0tezcGoFTm100y8HbwvC67l1uVx4PL5stwsQPq1w5u1e+hugi6goF488ch8xMUVo0KAhsbFl2bx5ExMmjKNIkSIMHnw/RYpEA/DYY8MpV64cF15Yk2PHjjF58kR27NjOK6+8TmxsLEBguwjw888/4vV66dq1W+A+0qcJvuzz+U+9sedt+121ajWSktrw888/4vP5KV68BC+99BpPPDGcJ59M3+s2Ojqae+65n08++ZCiRYsF1uOMZa969RpUr14DgO7de/Lgg/fyr3/9k3ff/bhQ3p8SE5sGrsvL+1Po9Sm7y3mfp5iYIoHWzPdpx3tufsxTo0bNQl5fWJ8jcpqnzL3h/NkoJiaGhg2bkJndy15e58nliqJRo+aB68NpfQrVe/rzGk7rU6jLuenNzWW75yk6OibocNGc569wtxGZW8NpfTp9nmJiimRZBsJpfTr9cpEiRYLeb0PNUzhtIzK/J4TT+hRqnk5fDsJpfSqsZe9MwupwtoxD1OLi4oKuz/ja6cOHD5/xULS9e/cGHeYWHx+f5duGMqbL/HgiIhK+LMtB6TLF0g8tjS1Oly6dSUk5wtixX/Dyyy8wa9ZMOnXqxLhx42jSpEFgusaNE/n99/m8+urLfPLJaGrWvJCvvvqKTp3aB6bJ/N+sWTOIi4vL9vaM/0qXKYZlOc5pnjLO35SaegKAmjUv4tNPx/LJJ2MZOfJ9vv12MlWrVubw4UNUrVotx/tr27YDa9asZuvWv86pKy+8Xg+LF88/9Vew8KZW+5jUq1Z7mNQKZvWq1R4mtYJZvWqNbGG1J1K9evWYN28eu3fvpmbNmoHrMwaMypYtS0JCAi6Xi5UrV9KtW7fANGlpaaxZs4auXbsGrqtTpw7z588nJSUl6OTay5alHztYt25du2dJRETOkWU5cDmtTIfeXQiX3085oNypaVYDgyZuAbZk+sk6WF3/RdVT/9oCPDJzH8ycG/qBWg8mFug58pdsWzIOv7MsBz5f3o8G37FjO9HRMUHfEudwOKhZ8yIg/USkW7b8hc/no1mzFjneX8YetoXzDW2OU/NxbgNrBUOt9jGpV632MKkVzOpVqz1MagWzetUaycJqEKlr1668++67fPPNN4Fv0gL45pv0c0G0aNGCkiVL0qpVKyZNmsSgQYMCg0MTJ07k+PHjdOnSJfBzXbp04cMPP2Ts2LHccccdQPpg07hx40hMTDzrb2YTEZHCE06H3uXWwYMHA4fOZdiwYT3JyT/TsuVlQYcWZubxuJk+fTpxceW48srOme7vALGxZU+b1sP06VOJiYmhRo2ap9+V7ZxOJ3XrNijwx80LtdrHpF612sOkVjCrV632MKkVzOpVa2QLq0GkSy65hOuuu45vv/0Wr9dL8+bNWbBgAdOnT+euu+6iQoUKADz44IP069eP/v37c/3117Nr1y5Gjx5NUlISV1xxReD+EhMT6dKlCyNGjGD//v1Ur16d8ePHs337dp599tnCmk0REYlg6edvSv9r1n/+Mzzb8zfde+/9gXMyPfbY0Cznb9q2bSsvv/w6pUqVDNz3yy8/z7FjKTRq1IT4+PLs37+f77//jr/+2sz99z9EqVIlgloyzt9kJ4/Hw6JFv9G0acug4+3DkVrtY1KvWu1hUiuY1atWe5jUCmb1qjWyhd2z9NRTT1GpUiXGjRvHDz/8QKVKlbJ8i1q9evUYPXo0L7/8Ms8//zzFixend+/ePPTQQ1nu76WXXuK1115j0qRJHD58mNq1a/POO+/QvHnzLNOKiIici4zzN7lOnWS9S5fOTJ48mbFjvyAlJYXY2Fg6derE4MGDqV69euDnGjdOZNy4cYEBpqZNm/Laa69mOey6V6+r+Oabb5gw4VsOHTpE8eLFqVevHkOHPkqHDh2y9Hi8Pg4fOm7rQJLD4aBs2XKBLxgIZ2q1j0m9arWHSa1gVq9a7WFSK5jVq9bIFnaDSFFRUQwePJjBgwefcbpmzZoxZsyYHO8vJiaGoUOHMnTo0PxKFBERCcn+8zeVgsa3U6YxlDl1zR5gxCoYsSp42vw6f1NOnE4ntWrVse3+85Na7WNSr1rtYVIrmNWrVnuY1Apm9ao1soXdIJKIiIjpTDx/U155PB7mz5/LpZdeHva7gavVPib1qtUeJrWCWb1qtYdJrWBWr1ojW+gzeoqIiIjkgmU5qFy5WuA8UOFMrfYxqVet9jCpFczqVas9TGoFs3rVGtk01CYiIiJ5ZllOatS4qLAzckWt9jGpV632MKkVzOpVqz1MagWzetUa2bQnkoiIiOSZx+NhzpwZeDyewk7JkVrtY1KvWu1hUiuY1atWe5jUCmb1qjWyaRBJRERE8syyLGrVqoNlhf9HCrXax6RetdrDpFYwq1et9jCpFczqVWtk0+FsIiIikmeWZVG5crXCzsgVtdrHpF612sOkVjCrV632MKkVzOpVa2TTcJuIiIiwePFCkpKahfxv5coVAOzcuSPbaZKSmvHii88E7m/NmlWMGPEiN998PR07JnHttd15/PFhbNnyV2HNIh6Pmx9+mIrH4y60htwyqRXM6lWrPUxqBbN61WoPk1rBrF61RjbtiSQiIiIBvXv3o27dS4Kuq1KlKgBlysTy+ONPB93m9/v5+ecf+fnnOTRv3jJw/eeff8yKFcto164jF11Ui/379zFu3NfcccfNjBo1mpo1L7Z/Zk5jWU4aNGiCZTkL/LHPlkmtYFavWu1hUiuY1atWe5jUCmb1qjWyaRBJREREAhITG9GuXceQtxUtWpTOnbtluX7atKkUL16c1q0vD1zXt+9NPPnks0RFRQWu69ChE7fe2o/PPvuYJ574b/7H58CyLCpUqFjgj5sXJrWCWb1qtYdJrWBWr1rtYVIrmNWr1simw9lEREQkyPHjx3L9LSW7du1k8eLfufzytsTExASub9AgMWgACaBq1WrUqFGTzZv/zNfe3HK73UybNgG3O/x3WTepFczqVas9TGoFs3rVag+TWsGsXrVGNu2JJCIiIgHPPfc0J04cx+l00rBhI+69dwh16lyS7fRz5szC7/fTqVPXHO/b7/dz8OABLrywZn4m55rT6aRFiySczvDfZd2kVjCrV632MKkVzOpVqz1MagWzetUa2TSIJCIiIkRFRdG2bXtatmxNmTJl+PPPPxkz5lMGDfoH77zzAQkJdUL+3MyZ3xMXV45mzVrk+BgzZkxj79493HHHXfmdnyuWZREXV65QHvtsmdQKZvWq1R4mtYJZvWq1h0mtYFavWiObDmcTERERGjRI5JlnXqJHj14kJbWhf/8BjBr1EQ4HvPPOyJA/s2XLX6xbt4YLL6yB1+s94/3/9ddmRox4kfr1G9K1aw87ZiFHbrebyZO/NmKXdZNawaxetdrDpFYwq1et9jCpFczqVWtk0yCSiIiIhFSlSlWSktqwZMnCkINEM2dOB6B//ztwubLfDXz//n38859DKF68BM8882Kh7TLucjlp06bTGVvDhUmtYFavWu1hUiuY1atWe5jUCmb1qjWy6XA2ERERyVaFChVwu92kpp6gePESQbfNnDmdatWq07Rp82x/PiUlhUceuZ+UlBRGjnyPcuXi7U7OlsNhUapU6UJ7/LNhUiuY1atWe5jUCmb1qtUeJrWCWb1qjWzaE0lERESytWPHdqKjYyhatFjQ9atWrWTbtq106NCJ8eO/DLkb+MmTJxk69EG2bt3CSy+9Wmgn1M7gdruzbQ03JrWCWb1qtYdJrWBWr1rtYVIrmNWr1simPZFERESEgwcPEhsbG3Tdhg3rSU7+mZYtL8Oygv/ulHEoW+fO3YiLi8PlCv5I4fV6efLJ4axcuZwXXniF+vUb2jsDueByuejSpVeW1nBkUiuY1atWe5jUCmb1qtUeJrWCWb1qjWx6pkRERM5jluXAshz85z/DiYkpQoMGDYmNLcvmzZuYMGEcRYoU4d5778fl+nsQyev1Mnv2TOrXb0D16tXwer04nRYOhyMwzZtvvkJy8s8kJV1BSspRZs6cFvS4Xbt2D/q3z+fH5/PbO7OAyxVl+2PkF5NawaxetdrDpFYwq1et9jCpFczqVWvk0uFsIiIi5ynLclC6TDFiY4vTpUtnUlKOMHbsF7z88gvMmjWTTp06MW7cOJo0aUBsbPHAf2vWLOPAgf1cfXUvypYtQXx8acqWLRE0zZ9//gFAcvLPPPXU41n+yzxtbGxxSpcphmU5cig+Nx6PhylTvsHj8dj6OPnBpFYwq1et9jCpFczqVas9TGoFs3rVGtm0J5KIiMh5yrIcuJwWQ8YsYeOhC+Hy+ykHlDt1+2pg0MQtwJYsP3vxve8z5iCMeWNu6DtvPpCLmw/M9rG7Z/q5i8uX4PV+jbEsh617I7lcLnr06G3ELusmtYJZvWq1h0mtYFavWu1hUiuY1avWyKZnSkRE5Dy3cU8Kq3YcKeyMAuHxuI35oGhSK5jVq1Z7mNQKZvWq1R4mtYJZvWqNXDqcTURERM4LHo+H6dMnGrHLukmtYFavWu1hUiuY1atWe5jUCmb1qjWyabhNREREjLN48ULuv//ukLe9885o6tdvAMDgwQNZunRx0O2vvPIKLVq0YsSIN7P87Lp1a/nww1EsX76MtLSTVKpUmZ49r6VPn375PxNnEBUVxTXX3FCgj3kuTOpVqz1MagWzetVqD5NawaxetUY2DSKJiIiIsXr37kfdupcEXVelStWgf5cvX4G77roXv99PauoJihQpSnx8+Sz3tWDBbwwd+iC1atVmwIA7KFq0GNu3b2Pv3t22zkMofr+Po0ePUrJkSRyO8N9x3KRetdrDpFYwq1et9jCpFczqVWtk07MkIiIixkpMbETnzt2C/itTpkzQNMWLF6dz52506NAJp9NPhw6daNq0edA0x46l8MwzT9KqVRLvvPMhffveRM+e13DPPfcxaNCQApyjdB6Pl59+moHH4y3wx84Lk3rVag+TWsGsXrXaw6RWMKtXrZFNeyKJiIiI0Y4fP0Z0dMwZT4rp8Xhwu91cdVWfkLfPnDmdAwf2M3DgICzL4sSJE8TExGBZhfP3tqioqGxbw5FJvWq1h0mtYFavWu1hUiuY1avWyKY9kURERMRYzz33NJ06taFDh9bcd99drF27Oss0W7du4corL6dTpyvo0eNK3n33/7KcQHPhwgUUL16cvXv3cMMN13LllZfTuXMbXn75eU6ePFlQsxPg8/nYv38fPp+vwB87L0zqVas9TGoFs3rVag+TWsGsXrVGNg0iiYiIiHGioqJo27Y9Q4Y8zAsvvMKdd97Dpk0bGTToH6xfvzYwXeXKVbjlltv5z3+eZfjwJ4iPj+eTTz7k6acfD7q/rVu34vV6GT78YS69tBXPPvsS3bv3ZMKEb3nuuacKevbwer0sWJCM12vG7vUm9arVHia1glm9arWHSa1gVq9aI5sOZxMRERHjNGiQSIMGiYF/JyW1oV27Dtx6az/eeWdk4JvXhg9/IujnunfvyYsvPsvkyeO5/vobA9/iduLEcVJTU7n66ut44IF/AtCmTXvcbjcTJ47jzjvvpmrVagU0d+mDZF27Xl1gj3euTOpVqz1MagWzetVqD5NawaxetUY27YkkIiIiEaFKlaokJbVhyZKFIf+i6PP52L17J3373gjAwoXzA7fFxMQA0LFj56CfufLKLgCsXLncruyQMlpN2b3epF612sOkVjCrV632MKkVzOpVa2TTIJKIiIhEjAoVKuB2u0lNPZHlNp/Py4oViylXrhwAR48eCdxWrlw8AGXLlg36mdjYsqemPWpXckgZrT6fGbvXm9SrVnuY1Apm9arVHia1glm9ao1sGkQSERGRiLFjx3aio2MoWrRYlttcrig6duzOnj27AShTJjZwW+3adQHYu3dv0M/s27c3y7QFIaPV5Yoq0MfNK5N61WoPk1rBrF612sOkVjCrV62RTYNIIiIiYpyDBw9muW7DhvUkJ/9MixaXYlkWx46lkJaWFrjd5/OxbdtffPTRBwC0aNEqcFv79h0BmDJlYtB9Tp48AafTSZMmTe2YjWz5fD62b99izO71JvWq1R4mtYJZvWq1h0mtYFavWiObTqwtIiIiRrAsB5blAOA//xlOTEwRGjRoSGxsWTZv3sSECeMoUqQI9957Py6XxcaN63niiX9x5ZWdqVKlKidOnGDatCls2vQHV199LfXqXRK470suuYSrrurF5MkT8fm8NG7clCVLFjFr1kxuvfU2LrigQmBan8+Pz+e3dV59Ph8bNqylQoVKWFb4/83PpF612sOkVjCrV632MKkVzOpVa2TTIJKIiIiEPctyULpMMVzO9A94Xbp0ZvLkyYwd+wUpKSnExsbSqVMnBg8eTPXq1QGoXbsmzZs3Y+7cOezbtw/LsqhZsyZPPfUUffv2xeFwBD3G888/S40a1Rg3bhw//zyHSpUqMXz4cAYMGBA0ncfr4/Ch47YOJLlcLtq27WTb/ec3k3rVag+TWsGsXrXaw6RWMKtXrZFNg0giIiIS9izLgctpMWTMEjbuSQEuhMvvpxxQ7tQ0q4FBE7cAW/7+wYt6U/Si3lQ99c+TwKd74NM3k7N5pESKXp1I9VP/+voIfP3G3MCtF5cvwev9GmNZjlwPIi1evJD777875G3vvDOa+vUbZLn+8OFD3HDDtRw5coT//vcF2rXrGLhtzZpVTJs2hcWLF7Fr1w5KlSpNvXoN+Mc/7qFatepZ7qsg+HxetmzZTLVqNbAsZ6E05JZa7WFSK5jVq1Z7mNQKZvWqNbJpEElERESMsXFPCqt2HMl5wjDUu3c/6ta9JOi6KlWqhpz2gw9GkZqaGvK2zz//mBUrltGuXUcuuqgW+/fvY9y4r7njjpsZNWo0NWtenO/tOfH5/GzfvoUqVaoT7kcDqNUeJrWCWb1qtYdJrWBWr1ojmwaRRERERApAYmKjoD2KsrNp00YmThzHbbf9g/fffyfL7X373sSTTz5LVNTf3yTToUMnbr21H5999jFPPPHffO3ODZfLRevW7Qr8cfNCrfYwqRXM6lWrPUxqBbN61RrZNNYmIiIiUkCOHz+Gx+M54zSvv/4Kl1/elri4siFvb9AgMWgACaBq1WrUqFGTzZv/zLfWs+H1etmwYS1er7dQHv9sqNUeJrWCWb1qtYdJrWBWr1ojmwaRRERERArAc889TadObejQoTX33XcXa9euzjLN7Nk/sGLFcu66616OHDmc6/v2+/0cPHiAMmXK5GNx7vn9fg4c2Iffb++31uUHtdrDpFYwq1et9jCpFczqVWtk0+FsIiIiIjaKioqibdv2tGzZmjJlyvDnn38yZsynDBr0D9555wMSEuoAcPJkKiNHvkbfvjdStWo16tbNesLt7MyYMY29e/dwxx132TUbZ+Ryubj00qRCeeyzpVZ7mNQKZvWq1R4mtYJZvWqNbNoTSURERMRGDRok8swzL9GjRy+SktrQv/8ARo36CIcD3nlnZGC6zz77GI/HQ//+t+H1etmyJXeHpv3112ZGjHiR+vUb0rVrD7tm44y8Xi9r1qww4nAAtdrDpFYwq1et9jCpFczqVWtk0yCSiIiISAGrUqUqSUltWLJkIV6vl507d/DFF58wcOAgihUrBvg5eTL0t7Nltn//Pv75zyEUL16CZ555EaezsL6e2M+JE8cBEw4HUKs9TGoFs3rVag+TWsGsXrVGMh3OJiIiIlIIKlSogNvtJjX1BO+//w7x8eVp3LgpO3fuACAurjwAhw4dYufOHVSocAFWpu8fTklJ4ZFH7iclJYWRI9+jXLn4QpkPAKfTRZMmlxba458NtdrDpFYwq1et9jCpFczqVWtk055IIiIiIoVgx47tREfHULRoMXbv3sW2bVu5/vpe9OnTkz59evKf/zwGwCuvvECfPj05duxY4GdPnjzJ0KEPsnXrFl566VUuvLBmYc0GkH44wPLli404HECt9jCpFczqVas9TGoFs3rVGtm0J5KIiIiIjQ4ePEhsbGzQdRs2rCc5+WdatrwMy7L4xz8GcfjwocDtPp+P+fPnMXnyRG688Rbq129I0aJFgfQPvE8+OZyVK5fzwguvUL9+w4KcHRERETmPaRBJRERExEZPPjmcmJgY6tdvSGxsWTZv3sSkSeMpUqQId999HwCJiY2y/FypUqWYPHkidetewhVXtA1c/9Zbr5Gc/DOtW1/OkSNH+P7774J+rnPnbnbOTkhOp5OGDZsU+OPmhVrtYVIrmNWrVnuY1Apm9ao1smkQSURERCSfWZYDy3IA0KZNO77/fhpjx37BsWPHiI0tQ9u27bnjjoFUrVot5M97vV62b/8LAKfTwuX6+wwEGzeuB2DevLnMmzc3y8927x78DW0+nx+fz94Thnq9HpYtW0RiYlOczvD+eKlWe5jUCmb1qtUeJrWCWb1qjWx6lkRERETykWU5KF2mGC5n+sDP3Xffyd1333nW93P77QO4/fYBWa4fM+aLs7ofj9fH4UPHbR5IclC0aDHAYeNj5Be12sOkVjCrV632MKkVzOpVayTTIJKIiIhIPrIsBy6nxZAxS9i4J6VQWy4uX4LX+zXGshy2DiI5nU7q1m1g2/3nJ7Xaw6RWMKtXrfYwqRXM6lVrZNMgkoiIiIgNNu5JYdWOI4WdUSA8Hg+LFv1G06YtcbnC++OlWu1hUiuY1atWe5jUCmb1qjWyWTlPIiIiIiKSPYfDQdmy5XA4wv9wALXaw6RWMKtXrfYwqRXM6lVrZNNQm4iIiIicE6fTSa1adQo7I1fUag+TWsGsXrXaw6RWMKtXrZFNeyKJiIiIyDnxeDzMm/cjHo+nsFNypFZ7mNQKZvWq1R4mtYJZvWqNbBpEEhEREZFzYlkOKleuhmWF/+EAarWHSa1gVq9a7WFSK5jVq9bIpsPZREREROScWJaTGjUuKuyMXFGrPUxqBbN61WoPk1rBrF61RjbtiSQiIiIi58Tj8TBnzgwjDgdQqz1MagWzetVqD5NawaxetUY2DSKJiIiIyDmxLItatepgWeH/0VKt9jCpFczqVas9TGoFs3rVGtl0OJuIiIiInBPLsqhcuVphZ+SKWu1hUiuY1atWe5jUCmb1qjWyabhNRERERM6Jx+Pmhx+m4vG4CzslR2q1h0mtYFavWu1hUiuY1avWyKZBJBERERE5J5blpEGDJliWs7BTcqRWe5jUCmb1qtUeJrWCWb1qjWw6nE1EREREzollWVSoULGwM3JFrfYwqRXM6lWrPUxqBbN61RrZtCeSiIiIiJwTt9vNtGkTcLvD/3AAtdrDpFYwq1et9jCpFczqVWtk0yCSiIiIiJwTp9NJixZJOJ3hfziAWu1hUiuY1atWe5jUCmb1qjWy6XA2ERERETknlmURF1eusDNyRa32MKkVzOpVqz1MagWzetUa2bQnkoiIiIicE7fbzeTJXxtxOIBa7WFSK5jVq1Z7mNQKZvWqNbJpEElEREREzonL5aRNm064XOF/OIBa7WFSK5jVq1Z7mNQKZvWqNbLpcDYREREROScOh0WpUqULOyNX1GoPk1rBrF612sOkVjCrV62RTXsiiYiIiMg5cbvdjB//pRGHA6jVHia1glm9arWHSa1gVq9aI5sGkURERETknLhcLrp06YXLFf47uavVHia1glm9arWHSa1gVq9aI5sGkURERETknLlcUYWdkGtqtYdJrWBWr1rtYVIrmNWr1silQSQREREROScej4cpU77B4/EUdkqO1GoPk1rBrF612sOkVjCrV62RTYNIIiIiInJOXC4XPXr0NuJwALXaw6RWMKtXrfYwqRXM6lVrZNMgkoiIiIicM4/HnJOSqtUeJrWCWb1qtYdJrWBWr1ojlwaRREREROSceDwepk+faMThAGq1h0mtYFavWu1hUiuY1avWyKZ9tkRERETknERFRXHNNTcUdkauqNUeJrWCWb1qtYdJrWBWr1ojm/ZEEhEREZFz4vf7OHLkMH6/r7BTcqRWe5jUCmb1qtUeJrWCWb1qjWwaRBIRERGRbH388QckJTWjf//rg673eDx8+OG79OnTi/btW3PDDdcyevT7IQ8JWLt2DQ89dB+dOrXhyiuv4MEH72XDhnUFNQtBPB4vP/00A4/HWyiPfzbUah+TetVqD5NawaxetUY2Hc4mIiIiIiHt2bObTz8dTdGiRbPc9vTTj/Pjjz/QvXtP6tSpy6pVK/nww3fZu3cvQ4c+Fphu3bq1DBp0J+XLV+C22/6B3+9j/PhvGDx4IO+99zHVqtUowDlKP3Thqqv6FOhj5pVa7WNSr1rtYVIrmNWr1simPZFEREREJKSRI1+jXr0G1K5dN+j6NWtWMXv2TG699Q6GDXucnj2v5e67B9O3701MmTKBjRs3BKZ9//23iYmJYdSoD7nhhpu58cZbePvtD/H5/IwaNbKgZwmfz8f+/fvw+cL/0AW12sekXrXaw6RWMKtXrZFNg0giIiIiksXSpYuZM2c299//cJbbli1bAkCHDp0A8Hq9LFiQTLt2HfD7/cyaNSPTtEtp1qwFpUuXCVxXrlw5Gjduwi+/JHP8+HF7Z+Q0Ga1eb/gfuqBW+5jUq1Z7mNQKZvWqNbJpEElEREREgni9Xl599X/06NGLiy66OMvtbrcbgJiYGCD9cICuXa+mePESQPohbH9PmxaYLrOYmCK43W42bfrDjlnIVkZrVFRUgT5uXqjVPib1qtUeJrWCWb1qjWwaRBIRERGRIBMmfMvu3Tu58857Qt5erVp1AFasWAakHw6we/dOli5dDMC+fXuCpl21akXQX3ndbjerV6/MMm1ByGg14dAFtdrHpF612sOkVjCrV62RLawGkebPn0/t2rVD/rd06dKgaRcvXswNN9xAYmIirVu35plnnuHYsWNZ7jMtLY3//e9/JCUl0bBhQ/r06cO8efMKaI5EREREzHL48CE++GAUt956J7GxsSGnadmyNRdcUJGRI1/np59ms2PHNr788hPee+9tnE4nJ0+eDEx7zTW92bp1Cy+88F/+/HMTmzZt5JlnnmD//n0AQdMWBJ/Py4oVi/H5wv/QBbXax6RetdrDpFYwq1etkS0sv52tf//+NGjQIOi6atWqBS6vWbOGAQMGcNFFFzFs2DB27drFhx9+yObNm3n//feDfm7YsGF8//333HLLLdSoUYPx48czcOBAPv74Y5o1a1Yg8yMiIiJiivfee5tSpUrRu3ffbKeJiYnhpZde44knhvPYY48CEB0dzT333M8nn3xI0aLFAtNefXVvdu/ezZdffsq0aVMAqFPnEm688ZYs0xYElyuKjh27F+hj5pVa7WNSr1rtYVIrmNWr1sgWloNIzZo1o0uXLtnePmLECEqVKsWnn35KiRLpx95XqVKFf//73yQnJ5OUlATA8uXLmTp1Ko8++ih33HEHAFdffTU9evTg5ZdfZsyYMfbPjIiIiIghtm7dwqRJ47n//ofYt29v4Pq0tDQ8Hg87d+6gePHilCpVmpo1L+LTT8fy55+bOHLkMEWLxlCt2oW8+eYIGjVqEnS/d911Lzfc0J8//9xEiRIluOiiiwPfzFa1ajUKks/nY+fObVSsWAXLCqud8rNQq31M6lWrPUxqBbN61RrZwvZZSklJwePxhLz+l19+oWfPnoEBJIBevXpRrFgxpk2bFrhu+vTpOJ1O+vb9+y9pMTEx9O7dmyVLlrBz5057Z0JERETEIHv37sHn8/Haay/Tp0/PwH+rV69k69Yt9OnTk9Gj/97r2+FwULPmRdSv35CdO7ezaNFCfD4fzZq1yHLfpUqVIjGxUeBE3QsXzqd8+QpUr16joGYPSP+FYcOGtUac/0Kt9jGpV632MKkVzOpVa2QLyz2Rhg8fzvHjx3E6nTRt2pRHH300cHjbunXr8Hg81K9fP+hnoqOjqVu3LmvWrAlct2bNGmrUqBE02ATQsGHDwO0VK1a0eW5EREREzFCz5sU899zLWa5/773/4/jx4wwZ8giVK1fJcrvL5aJVqyu45547iYsrx5VXdj7j48yaNYM1a1Zz770PFPhffl0uF23bdirQx8wrtdrHpF612sOkVjCrV62RLawGkaKioujcuTNXXHEFsbGx/PHHH3zwwQfcdNNNjBkzhksuuYS9e9N3rS5fvnyWn4+Pj2fRokWBf+/du5f4+PiQ0wHs2XP23wbi9XqwLFfgG0acTiderwdw4HQ68Xg8OBx/X7YsB5aVcdnCsiw8HjeW5cSyLNxuN07n35ddLicOR8bl9JfH4/EEXY6KisLv9+HxeImKisLn8+H1/n3Z5/PicmVc9uFyufD5vHg8/rOeX7v5/X7cbnfIeQo3GU2hXie/P7yeW4/Hi8/ny2bZC7+vr/R605fhrOtTeJ7gzufz4vP5cbnStwV+f8bl8Fpu0/fmtLLZRmT9uu3CdqbtXjgKtS0Pxx18M5bLUO9P4dbr9foCr336+6wfpzP9ssNR2HVZZf+eG17bAkh/v/X7/Tl+jihVqhSXXdY6yzx99dUX+P1wxRVt8Xq9eDwennrq38TFxVG9+oUcP36MiRPHsXfvXv73v9eIiYkJbNcXLfqdTz75kBYtWlKiREnWrFnFtGlTaNGiJddddz0Qen2y67ORx+Nmx45tVKlSFbBCbMuDlz0ovM97Xq+bzZv/5MILLwIcufq8F/r9yf55sqz0wyErVqxMdHTMOb9Ods8T+Nm2bQuVKlXG5You8M/lZzNPbvdJtm7dQo0aNfH5/GH7u4bP58eyHGzevIkqVaoRHR0TVuvT6fPkdqexc+d2qlRJP6Q2nNanUPOUlnaSHTu2Ua1aDXw+f1itT6fPE/j5888/qF79QlyuqLBan06fJ7/fx/btW6lUqQoulyus1qfCWPZyI6w+PTZp0oQ33niD3r1706FDBwYOHMhXX32Fw+HglVdeASA1NRVI3/PodDExMYHbM6bNbrrM93U2VqxYCsCqVctYtSr9a22XLVvE+vWrAVi06Dc2bdoAwPz5c9myZTMAycmz2blzGwBz5sxg797dAPzww1QOHjwAwPTpEzh69CgAU6Z8Q2rqCTweD1OmfIPH4yE19QRTpnwDwNGjR5k+fQIABw8e4IcfpgKwd+9u5syZAcDOndtITp4NwJYtm1m5cslZz6/dTp48GXKejhw5UohVoWU0hXqdMn9tcThYuXJJtstexvIWTjZtWh9yfVqzZkVhZmVry5bNzJ8/F4BNmzawaNFvp67fVJhZWfzyyxwg+21EuMluu/fzzz8UdlpI2W3Lw03Gcpnd+1M42b59S2B9Wr9+NcuWpf9haNWqZWzatL4w00LK7j13164dhZkVktfrzfZzxNGjh1m8+FdcLoujRw+xbNnvuFwWhw7tY+XKxbhcFm53GmlpqbhcFrt3b2fDhtVccskl/PJLMm+88QqfffYxsbGxPPHEk7Rs2ZJNm9azY8dfuFwWKSmH8XjcfPnlp7z66kssWbKIu+4axI033khKyiFcLotly37n6NFDWJbD9s9Gv/wyh+3bt/DXX6G35acve4X5ee/IkSOnvjXIn+vPe4U1T0eOHGH79i189934fP8Ma8c87dixje3bt/DTT7MK5XP52czT8uVL2LBhDT6fP6x/15g/fy4+n58//ljH4sUL8uV1snOeZs+ezvbtW9izJ/zWp1Dz9N1349m27S+OHz8eduvT6fPk8/lZuXIJBw6E3/qUdZ42sn37FhYsmBd265Pdr9Pp85TRlROHP9x2oQjhoYceYsaMGSxbtoyZM2cyZMgQPv/88yzfrjZkyBAWLVpEcnIyAD169CAuLo6PP/44aLqNGzfSvXt3nnrqKfr163dWLbt3HzR2TyTLsoiPL0X3N+ayakfhDtLUq1SKqfdfzoEDKaSmpmWZJ8uCuLiSYdW6f/9RfL7QeyK5XBZly5Yo9N6M1r17j2S7J1J0dFTYPbf79h3F48m6JxL4w26ZPXjwGGlp7pB/HXA4oFy5wn9u/14ODpPdnkhFisQQG1s8bFoPHjzGiRMnQ273HA4/8fGlC70VgntTU09m2ZZHR7vC4nnN3Lpv31H8/tB7IkVHR4VFb+ZWtzv0nkgulzOs1q/0bYEn5HtuOL6HHTiQgsfjy/I5IiYmmtJliuFyhsffFT1eH/v3HQEcEf2XXs2T5knzpHnSPGmewm2ePB43FSuWPfMbNWF2OFt2LrjgAtxuNydOnDjjoWh79+4NOswtPj6e3bt3h5wOQh8SlxOn04Xfz6lfcP++LkPGAnPmy38fThQVlbfLDodFVFT6B76MheHMl524XOHxATEzh8OR7TyFG8tK/2U89OsRXsdZuFxOPB7HqcvBy144PrdOp0XGcHbw+uTM5icKV/pGN/1y8LYgvJ5bl8uFx+PLdrsQbrLb7oXrcpDdtjzcOJ0WHo8v2/ekcJK+LXCdupx53XKG3foFZ37/DTcOhyPke65lOXA5LYaMWcLGPSmF2nhx+RK83q8x0dFReDzphwTa8dnI7/ezYcNaatasFVgXTl/eQl8u+M97Pp+fzZv/oGbNWoEP+Weev+zen+yfJ6/Xy6ZN6c9rxuei/PoMa8c8eb3ewHKQ8VgF+bn8bOYJHGzatCFomT3z/BXO7xqWxanlIL31zPNX+L8/BW8LorKdpzPPR8HNk2VZbNy4jpo1a4Xd+nT6PHm93sC2K7fzV1jzFLwtcGY7T2c7H4U5Tzlfzrn9TMLzU+Rptm3bRkxMDMWKFSMhIQGXy8XKlSvp1q1bYJq0tDTWrFlD165dA9fVqVOH+fPnk5KSEnRy7WXL0nf7qlu3bsHNhIiIiEiY2rgnpdD3mioofr+fAwf2ceGFFxd2So7Uah+TetVqD5NawaxetUa2sPpTWcYxk5mtXbuW2bNn07p1ayzLomTJkrRq1YpJkyaRkvL3X8wmTpzI8ePH6dKlS+C6Ll264PV6GTt2bOC6tLQ0xo0bR2Jior6ZTUREROQ843K5uPTSpLDdIy8ztdrHpF612sOkVjCrV62RLayeqQceeIAiRYrQuHFj4uLi2LhxI1999RVFihThkUceCUz34IMP0q9fP/r378/111/Prl27GD16NElJSVxxxRWB6RITE+nSpQsjRoxg//79VK9enfHjx7N9+3aeffbZwphFERERESlEXq+X9etXk5BwyWmHDIUftdrHpF612sOkVjCrV62RLawGkTp27MjkyZP56KOPSElJITY2liuvvJLBgwdTvXr1wHT16tVj9OjRvPzyyzz//PMUL16c3r1789BDD2W5z5deeonXXnuNSZMmcfjwYWrXrs0777xD8+bNC3LWRERERCQs+Dlx4jgQ9t8tg1rtZFKvWu1hUiuY1avWSBZWg0i33HILt9xyS66mbdasGWPGjMlxupiYGIYOHcrQoUPPNU9EREREDOd0umjS5NLCzsgVtdrHpF612sOkVjCrV62RLazOiSQiIiIiYiev18vy5YsDX48cztRqH5N61WoPk1rBrF61RjYNIomIiIiIiIiISI7C6nA2ERERERE7OZ1OGjZsUtgZuaJW+5jUq1Z7mNQKZvWqNbJpTyQREREROW94vR4WL56P1+sp7JQcqdU+JvWq1R4mtYJZvWqNbBpEEhEREZHziIOiRYsBjsIOyQW12sekXrXaw6RWMKtXrZFMh7OJiIiIyHnD6XRSt26Dws7IFbXax6RetdrDpFYwq1etkU17IomIiIjIecPj8TB/fjIeT/gfuqBW+5jUq1Z7mNQKZvWqNbJpEElEREREzhsOh4OyZcvhcIT/oQtqtY9JvWq1h0mtYFavWiObDmcTERERkfOG0+mkVq06hZ2RK2q1j0m9arWHSa1gVq9aI5v2RBIRERGR84bH42HevB+NOHRBrfYxqVet9jCpFczqVWtk0yCSiIiIiJw3LMtB5crVsKzwP3RBrfYxqVet9jCpFczqVWtk0+FsIiIiInLesCwnNWpcVNgZuaJW+5jUq1Z7mNQKZvWqNbJpTyQREREROW94PB7mzJlhxKELarWPSb1qtYdJrWBWr1ojmwaRREREROS8YVkWtWrVwbLC/2OwWu1jUq9a7WFSK5jVq9bIpsPZREREROS8YVkWlStXK+yMXFGrfUzqVas9TGoFs3rVGtk03CYiIiIi5w2Px80PP0zF43EXdkqO1Gofk3rVag+TWsGsXrVGNg0iiYiIiMh5w7KcNGjQBMtyFnZKjtRqH5N61WoPk1rBrF61RjYdziYiIiIi5w3LsqhQoWJhZ+SKWu1jUq9a7WFSK5jVq9bIpj2RREREROS84Xa7mTZtAm53+B+6oFb7mNSrVnuY1Apm9ao1smkQSURERETOG06nkxYtknA6w//QBbXax6RetdrDpFYwq1etkU2Hs4mIiIjIecOyLOLiyhV2Rq6o1T4m9arVHia1glm9ao1s2hNJRERERM4bbrebyZO/NuLQBbXax6RetdrDpFYwq1etkU2DSCIiIiJy3nC5nLRp0wmXK/wPXVCrfUzqVas9TGoFs3rVGtl0OJuIiIiInDccDotSpUoXdkauqNU+JvWq1R4mtYJZvWqNbNoTSURERETOG263m/HjvzTi0AW12sekXrXaw6RWMKtXrZFNg0giIiIict5wuVx06dILlyv8d8hXq31M6lWrPUxqBbN61RrZNIgkIiIiIucVlyuqsBNyTa32MalXrfYwqRXM6lVr5NIgkoiIiIicNzweD1OmfIPH4ynslByp1T4m9arVHia1glm9ao1sGkQSERERkfOGy+WiR4/eRhy6oFb7mNSrVnuY1Apm9ao1smkQSURERETOKx6POSdQVat9TOpVqz1MagWzetUauTSIJCIiIiLnDY/Hw/TpE404dEGt9jGpV632MKkVzOpVa2TTPlsiIiIict6IiorimmtuKOyMXFGrfUzqVas9TGoFs3rVGtm0J5KIiIiInDf8fh9HjhzG7/cVdkqO1Gofk3rVag+TWsGsXrVGNg0iiYiIiMh5w+Px8tNPM/B4vIWdkiO12sekXrXaw6RWMKtXrZFNh7OJiIiIyHkjKiqKq67qU9gZuaJW+5jUq1Z7mNQKZvWqNbJpTyQREREROW/4fD7279+Hzxf+hy6o1T4m9arVHia1glm9ao1sGkQSERERkfOG1+tlwYJkvN7wP3RBrfYxqVet9jCpFczqVWtk0+FsIiIiInLeiIqKomvXqws7I1fUah+TetVqD5NawaxetUY27YkkIiIiIucNn8/H7t07jTh0Qa32MalXrfYwqRXM6lVrZNMgkoiIiIicN3w+LytWLMbnC/9DF9RqH5N61WoPk1rBrF61RjYdziYiIiIi5w2XK4qOHbsXdkauqNU+JvWq1R4mtYJZvWqNbNoTSURERETOGz6fj+3btxhx6IJa7WNSr1rtYVIrmNWr1simQSQREREROW/4fD42bFhrxC8MarWPSb1qtYdJrWBWr1ojmw5nExEREZHzhsvlom3bToWdkStqtY9JvWq1h0mtYFavWiOb9kQSERERkfOGz+dl8+Y/jDiJqlrtY1KvWu1hUiuY1avWyKZBJBERERE5b/h8/lPnv/AXdkqO1Gofk3rVag+TWsGsXrVGNh3OJiIiIiLnDZfLRevW7Qo7I1fUah+TetVqD5NawaxetUY27YkkIiIiIucNr9fLhg1r8XrD/9AFtdrHpF612sOkVjCrV62RTYNIIiIiInLe8Pv9HDiwD78//A9dUKt9TOpVqz1MagWzetUa2XQ4m4iIiIicN1wuF5demlTYGbmiVvuY1KtWe5jUCmb1qjWyaU8kEREREYkIH3/8AUlJzejf//rAdampqXz77Vc8+OC99OrVmSuvvIIbbriWb7/9KuThC/v27ePFF5+lT5+etG/fmuuv78Wbb47g8OFDBTgn6bxeL2vWrDDiMAuTWsGsXrXaw6RWMKtXrZFNeyKJiIiIiPH27NnNp5+OpmjRokHX79ixjdde+x9Nmzanb9+bKFq0KDNnTufVV19izZpV/PvfTwWmPX78OHfffRupqSe45po+lC9fgY0b1/Ptt1+xePFCPvjgMyyrIP8G6+fEieOACYdZmNQKZvWq1R4mtYJZvWqNZBpEEhERERHjjRz5GvXqNcDr9QbtNVS2bDk+/ngMNWteFLjummv68NxzT/Hdd5MZMOBOqlSpCkBy8s/s2rWTl156jcsu+/vwhlKlSjN69Hts3LiehIQ6BTZPTqeLJk0uLbDHOxcmtYJZvWq1h0mtYFavWiObDmcTEREREaMtXbqYOXNmc//9D2e5rUyZMkEDSF6vl+XLF5OU1AaAzZv/DNx2/HgKAGXLlg26j7i4OABiYorke/uZZLSacJiFSa1gVq9a7WFSK5jVq9bIpkEkERERETGW1+vl1Vf/R48evbjoootz/XMHDuwH0geZMiQmNsGyLF5//WVWrlzBnj27+fXXZD75ZDSXX96W6tVr5HO9iIiIWXQ4m4iIiIgYa8KEb9m9eyevvfZ/uZre6XRSt24DXnrpBSpWrEydOpcEbrvwwpo8+ui/eOut17n77tsC13ft2oOhQ/+d7+25aW3YsEmBP25emNQKZvWq1R4mtYJZvWqNbNoTSURERESMdPjwIT74YBS33nonsbGxufoZr9fDY489wubNm3jooX/icgX/TbVcufJcckk97r//YZ577mX69r2JGTOm8c47b9kxCzm2Ll48H6/XU+CPfbZMagWzetVqD5NawaxetUY27YkkIiIiIkZ67723KVWqFL179831z3z55Wf88ss87rjjLlq1Sgq6bfnypQwd+iCjRo0O7KF0xRVtKV68OKNHv0f37j258MKa+ToPZ+agaNFigKMAHzOvTGoFs3rVag+TWsGsXrVGMu2JJCIiIiLG2bp1C5Mmjad3777s27eXnTt3sHPnDtLS0vB4POzcuYMjRw4H/cx3301m1KiRXH31ddx22z+y3OfEieOIjS0bdIgbQFLSFfj9flauXG7rPJ0u49A7p9NZoI+bFya1glm9arWHSa1gVq9aI5sGkURERETEOHv37sHn8/Haay/Tp0/PwH+rV69k69Yt9OnTk9Gj3w9MP3fuHF588Rkuv7wtrVu3xuPJeujCwYMH8Pl8Wa7PmLagD3fweDzMn58csjXcmNQKZvWq1R4mtYJZvWqNbDqcTURERESMU7PmxTz33MtZrn/vvf/j+PHjDBnyCJUrVwFg6dLFPPnkYyQmNubxx59m27a/cDiyHrpQtWo1Fiz4jcWLF9KkSbPA9T/88D0AtWrVsWluQnM4HJQtWy5ka7gxqRXM6lWrPUxqBbN61RrZNIgkIiIiIsawLAeW5aBcubK0b98+y+1ff/0lDocjcNvOnTsYNuyhU9d1JDl5DgCbN/8JwMUX16JWrQQArr++H999N5lhwx6iT5++XHBBRZYsWcyMGdNp0aIliYkNA4/j8/nx+fy2zqvT6Szwgau8MqkVzOpVqz1MagWzetUa2TSIJCIiIiJGsCwHpcsUw+XM/owMUVFOnE6L2NjiAKxff4CUlBQAXn75hSzTDx48mBYtGgMQG1uPcePG8dprrzFjxnT27dtH+fLluf3227n//vspWrRo4Oc8Xh+HDx23dSAp/TCLuVx66eVZvkUu3JjUCmb1qtUeJrWCWb1qjWx6lkRERETECJblwOW0GDJmCRv3pISeqPlAALq/MTdw1cX3vh96WmC6H6Znmjb9B/pQ/OI+FD/1z5+Bn99b+PfN5Uvwer/GWJbD1kEky3JQuXI1LCv8D7MwqRXM6lWrPUxqBbN61RrZNIgkIiIiIkbZuCeFVTuOFHaG7SzLSY0aFxV2Rq6Y1Apm9arVHia1glm9ao1s+nY2EREREZEw5PF4mDNnhhHfGmRSK5jVq1Z7mNQKZvWqNbJpEElEREREJAxZlkWtWnWwrPD/yG5SK5jVq1Z7mNQKZvWqNbLpcDYRERERkTBkWRaVK1cr7IxcMakVzOpVqz1MagWzetUa2TTcJiIiIiIShjweNz/8MBWPx13YKTkyqRXM6lWrPUxqBbN61RrZNIgkIiIiIhKGLMtJgwZNsCxnYafkyKRWMKtXrfYwqRXM6lVrZNPhbCIiIiIiYciyLCpUqFjYGbliUiuY1atWe5jUCmb1qjWyaU8kEREREZEw5Ha7mTZtAm53+B9mYVIrmNWrVnuY1Apm9ao1smkQSUREREQkDDmdTlq0SMLpDP/DLExqBbN61WoPk1rBrF61RjYdziYiIiIiEoYsyyIurlxhZ+SKSa1gVq9a7WFSK5jVq9bIpj2RRERERETCkNvtZvLkr404zMKkVjCrV632MKkVzOpVa2TTIJKIiIiISBhyuZy0adMJlyv8D7MwqRXM6lWrPUxqBbN61RrZdDibiIiIiEgYcjgsSpUqXdgZuWJSK5jVq1Z7mNQKZvWqNbJpTyQRERERkTDkdrsZP/5LIw6zMKkVzOpVqz1MagWzetUa2TSIJCIiIiIShlwuF1269MLlCv+DB0xqBbN61WoPk1rBrF61RjYNIomIiIiIhCmXK6qwE3LNpFYwq1et9jCpFczqVWvk0iCSiIiIiEgY8ng8TJnyDR6Pp7BTcmRSK5jVq1Z7mNQKZvWqNbJpEElEREREJAy5XC569OhtxGEWJrWCWb1qtYdJrWBWr1ojmwaRRERERETClMdjzsleTWoFs3rVag+TWsGsXrVGLg0iiYiIiIiEIY/Hw/TpE404zMKkVjCrV632MKkVzOpVa2QL60Gkt99+m9q1a9OjR48sty1evJgbbriBxMREWrduzTPPPMOxY8eyTJeWlsb//vc/kpKSaNiwIX369GHevHkFkS8iIiIikmdRUVFcc80NREWF/0lfTWoFs3rVag+TWsGsXrVGtrAdRNq1axejRo2iWLFiWW5bs2YNAwYMIDU1lWHDhtG7d2/Gjh3LkCFDskw7bNgwPvroI6666ioee+wxnE4nAwcOZOHChQUxGyIiIiIieeL3+zhy5DB+v6+wU3JkUiuY1atWe5jUCmb1qjWyhe0g0osvvkhiYiL169fPctuIESMoVaoUn376KTfccAMPPvggTzzxBHPnziU5OTkw3fLly5k6dSoPPfQQQ4cOpW/fvnz88cdUqlSJl19+uSBnR0RERETkrHg8Xn76aQYej7ewU3JkUiuY1atWe5jUCmb1qjWyheUg0u+//87333/Pv/71ryy3paSk8Msvv9CzZ09KlCgRuL5Xr14UK1aMadOmBa6bPn06TqeTvn37Bq6LiYmhd+/eLFmyhJ07d9o7IyIiIiIieRQVFcVVV/Ux4jALk1rBrF612sOkVjCrV62RLewGkbxeL//973/p3bs3tWvXznL7unXr8Hg8WfZQio6Opm7duqxZsyZw3Zo1a6hRo0bQYBNAw4YNA7eLiIiIiIQjn8/H/v378PnC/zALk1rBrF612sOkVjCrV62RLewGkcaMGcOOHTt44IEHQt6+d+9eAMqXL5/ltvj4ePbs2RM0bXx8fMjpgKBpc8vr9Zz6vxev1xu4LuOyxxN82efLfNl36rI7cNntDr6ccSxm+mU/fr8/y2VIP3Yz47LPF3w54ysK0y97Tl32huUuemeap3CT0+sUTjweb47LXjjxen3ZrE/ht8xCxvr097bg78vh9dz+vf6H3kaEm+y2e+H6bRnZbcvDTcZymd37Uzjxen1B61bwe274Pb/Zv+eGX+uZPkeEm8zvW6Hec8ONnZ/3PB4P8+fPxev1nsXnvVDvT6evT/n/Gdbj8bBgQTKpqak2fIbN/3lyu90sWJDMyZMnC+lzee7nKS0tLbAchPfvGult8+fPJS3tZL68TnbO08mTqSxYkHxq+Q2v9SnUPKWmpjJ//tzA8nsur5Pd85SxHGT0hNP6dPo8paWlZdoWhNf6ZPfrFGqeciOsBpEOHjzIG2+8waBBgyhbtmzIaVJTU4H0PY9OFxMTE7g9Y9rspst8X2djxYqlAKxatYxVq5YBsGzZItavXw3AokW/sWnTBgDmz5/Lli2bAUhOns3OndsAmDNnBnv37gbghx+mcvDgAQCmT5/A0aNHAZgy5RtSU0/g8XiYMuUbPB4PqaknmDLlGwCOHj3K9OkTADh48AA//DAVgL17dzNnzgwAdu7cRnLybAC2bNnMypVLznp+7Xby5MmQ83TkyJFCrAotoynU65SxooaLlSuXZLvsZSxv4WTTpvUh16c1a1YUZla2tmzZzPz5cwHYtGkDixb9dur6TYWZlcUvv8wBst9GhJvstns///xDYaeFlN22PNxkLJfZvT+Fk+3btwTWp/XrV7Ns2SIg/T1306b1hZkWUnbvubt27SjMrJC8Xm+2nyPCzfz5ydl+Njp58mRhpoVk5+e99M8YHqKionL9eS/U+9Pp65Mdn2FTU0/QtevVfP/9xHz/DGvHPO3bt5uuXa9m3rzZhfK5/GzmafXqZVx44cVERUWF9e8a8+fPJSoqilq16gbmoyCWvbzO05w5M+ja9WoOHToQdutTqHn6/vuJXHllD7xeT9itT6fPU1RUFA6Hg5SUo+f8Otk9T1u3bqZr16tZvPi3sFuf7H6dTp+njK6cOPxh9CedJ598kl9//ZUpU6YEBn/69+/PwYMHmTJlCpB+nqMhQ4bw+eef06xZs6CfHzJkCIsWLQqcXLtHjx7ExcXx8ccfB023ceNGunfvzlNPPUW/fv3OqnH37oNYliswaOB0Ok+NBjpwOp14PB4cjr8vW5YDy8q4bGFZFh6PG8tyYlkWbrcbp/Pvyy6XE4cj47ILSB81zHw5KioKv9+Hx+MlKioqMNqbcdnn8+JyZVz24XK58Pm8WJZFfHwpur8xl1U7CneQpl6lUky9/3IOHEghNTUtyzxZFsTFlQyr1v37j+LzEfJ1crksypYtUei9Ga179x7B5/OFXPaio6PC7rndt+8oHo83y/oE/rBbZg8ePEZamhufz4/Llb4t8PvTLzscUK5c4T+3fy8HhwEr5DaiSJEYYmOLh03rwYPHOHHiZMjtnsPhJz6+dKG3QnBvaurJLNvy6GhXWDyvmVv37TuK30/I96fo6Kiw6M3c6nZ7AusW+HE60y+7XM6wWr/StwWekO+54fgeduBACh6PL8vniKJFw2NbkLl1797D+HyE/GxUpEh0WL3fpm8L0mz7vOf1eti1aycVK1YGyNXnvVDvT6evT5D/n2Ety8G+fXspU6Zs4HN8fn2GtWOeAPbv30tsbFlcrqgC/1x+NvPkdqexb99eKlSoiM/nC9vfNXw+P5ZlsXv3TuLi4omOji6QZS+v8+R2uzl06ABxcfGAP6zWp1DzlJaWxsGD+4mPr4DP5wur9en0eQLYtWs75ctXxOVyhdX6dPo8+f1+DhzYR2xsHC6XM6zWp4Je9jweNxUrht6ZJ7Ow2RNp8+bNfPXVV/Tv3589e/awbds2tm3bxsmTJ3G73Wzbto1Dhw6d8VC0vXv3Bh3mFh8fHzj87fTpIPQhcTlxOl2n/u889Utu+nUZl12u4MsZK1H6ZevU5ajA5fQBk78vOxyZLztwOBxZLgM4HFbgsmUFX3a5Ml92nbrsxOVynvX82u1M8xRucnqdwknGBjD9cuhlL5w4nVY261P4LbOQsT79vS34+3J4Pbd/r/+htxHhJrvtXsZ8hJvstuXhJmO5zO79KZw4nVbQuhX8nht+z2/277nh13qmzxHhJvP7Vqj33HBj5+c9v9/PmjXLA38MzN3nvVDvT6evT/n/Gdbv97NixWKcTsuGz7D5P0+Q3pvxS9W5vE52z5PD4WDVqqWnftkM59810n/5XbVqKZblyJfXyc55cjotVqxYTMYA0rm+TnbPk9NpsXLlEvx+X9itT6fPk8/nZfXq5YD/nF8nu+fJ4eDUtsARduuT3a9TqHnKjbD5lLN79258Ph/PPPMMHTp0CPy3bNkyNm/eTIcOHRg5ciQJCQm4XC5WrlwZ9PNpaWmsWbOGOnXqBK6rU6cOmzdvJiUlJWjaZcvSd/uqW7eu/TMmIiIiIpIHLlcUHTt2D9vB/8xMagWzetVqD5NawaxetUa2sBlEqlWrFiNHjszyX61atahUqRIjR46kd+/elCxZklatWjFp0qSgwaGJEydy/PhxunTpEriuS5cueL1exo4dG7guLS2NcePGkZiYSMWKFQt0HkVEREREcsvn87F9+5bASU/DmUmtYFavWu1hUiuY1avWyBY2xwiULVuWjh07Zrk+43xGmW978MEH6devH/379+f6669n165djB49mqSkJK644orAdImJiXTp0oURI0awf/9+qlevzvjx49m+fTvPPvus/TMlIiIiIpJHPp+PDRvWUqFCpbA8TDIzk1rBrF612sOkVjCrV62RLWwGkc5GvXr1GD16NC+//DLPP/88xYsXp3fv3jz00ENZpn3ppZd47bXXmDRpEocPH6Z27dq88847NG/evBDKRURERERyx+Vy0bZtp8LOyBWTWsGsXrXaw6RWMKtXrZEtT4NIO3bsYMeOHUHfjrZ27Vo+/PBD0tLS6NGjR8i9ivLi008/DXl9s2bNGDNmTI4/HxMTw9ChQxk6dGi+9IiIiIiIFASfz8uWLZupVq1GWJ4MPzOTWsGsXrXaw6RWMKtXrZEtT/trPfPMM7z11luBf+/bt49bbrmFmTNnsnDhQu677z5mzJiRb5EiIiIiIucbn89/6lwd/sJOyZFJrWBWr1rtYVIrmNWr1siWp0Gk5cuXc9lllwX+PWHCBFJTU5k4cSI///wzrVq14sMPP8y3SBERERGR843L5aJ163aBr3cOZya1glm9arWHSa1gVq9aI1ueBpEOHz5MXFxc4N9z5syhefPmVKtWDcuyuPLKK9m0aVO+RYqIiIiInG+8Xi8bNqzF6/UWdkqOTGoFs3rVag+TWsGsXrVGtjwNIpUtW5YdO3YAcOTIEZYuXcrll18euN3r9eLxePKnUERERETkPOT3+zlwYB9+f/gfZmFSK5jVq1Z7mNQKZvWqNbLlaZ+tyy67jE8//ZQSJUowf/58/H4/HTp0CNy+ceNGKlasmG+RIiIiIiLnG5fLxaWXJhV2Rq6Y1Apm9arVHia1glm9ao1sedoT6eGHH6ZmzZq8+OKLzJs3j0cffZSqVasCkJaWxrRp02jVqlW+hoqIiIiInE+8Xi9r1qww4jALk1rBrF612sOkVjCrV62RLU97IpUrV44xY8Zw9OhRYmJiiI6ODtzm8/n4+OOPueCCC/ItUkRERETk/OPnxInjgAmHWZjUCmb1qtUeJrWCWb1qjWRnvSfSiRMnuPbaa/nyyy8pWbJk0AASQJEiRahTpw5lypTJr0YRERERkfOO0+miSZNLcTrD/1uDTGoFs3rVag+TWsGsXrVGtrMeRCpatCjbtm3D4XDY0SMiIiIiIqQfZrF8+WIjDrMwqRXM6lWrPUxqBbN61RrZ8nROpMsvv5zk5OT8bhERERERERERkTCVp0GkQYMGsXnzZv75z3+ycOFCdu/ezaFDh7L8JyIiIiIieeN0OmnYsAlOp7OwU3JkUiuY1atWe5jUCmb1qjWy5WkQqXv37mzcuJHJkyfTv39/2rZtS6tWrbL8JyIiIiIieeP1eli8eD5er6ewU3JkUiuY1atWe5jUCmb1qjWy5ensUffee6/OiSQiIiIiYisHRYsWA0z43G1SK5jVq1Z7mNQKZvWqNZLlaRDpvvvuy+8OERERERHJxOl0Urdug8LOyBWTWsGsXrXaw6RWMKtXrZEtT4ezne7o0aM6m7mIiIiISD7yeDzMn5+MxxP+h1mY1Apm9arVHia1glm9ao1seR5EWrFiBXfccQeJiYlceumlLFiwAIADBw5wzz33MH/+/HyLFBERERE53zgcDsqWLWfEaSRMagWzetVqD5NawaxetUa2PA0iLV68mBtvvJG//vqLnj174vP5AreVLVuWlJQUxo4dm2+RIiIiIiLnG6fTSa1adYz41iCTWsGsXrXaw6RWMKtXrZEtT4NIr776KhdddBHfffcdDz74YJbbL730UpYtW3bOcSIiIiIi5yuPx8O8eT8acZiFSa1gVq9a7WFSK5jVq9bIlqdBpBUrVnDttdcSHR0dcrevChUqsG/fvnOOExERERE5X1mWg8qVq2FZ4X+YhUmtYFavWu1hUiuY1avWyJanb2dzuVxBh7Cdbvfu3RQrVizPUSIiIiIi5zvLclKjxkWFnZErJrWCWb1qtYdJrWBWr1ojW572REpMTOT7778Pedvx48cZN24czZs3P6cwEREREZHzmcfjYc6cGUYcZmFSK5jVq1Z7mNQKZvWqNbLlaRDp/vvvZ+XKlQwcOJCff/4ZgHXr1vH1119z7bXXcuDAAQYNGpSvoSIiIiIi5xPLsqhVqw6WlecvVC4wJrWCWb1qtYdJrWBWr1ojW54OZ0tMTOTdd9/lP//5D0OHDgXghRdeAKBatWq8++671KlTJ/8qRURERETOM5ZlUblytcLOyBWTWsGsXrXaw6RWMKtXrZEtz8NtrVq14vvvv2f8+PG8+uqrjBgxgq+//prvv/+eFi1a5GejiIiIiMh5x+Nx88MPU/F43IWdkiOTWsGsXrXaw6RWMKtXrZEtT3siZVa3bl3q1q2bHy0iIiIiInKKZTlp0KAJluUs7JQcmdQKZvWq1R4mtYJZvWqNbLkaRPr9998BAifLzvh3TnRybRERERGRvLEsiwoVKhZ2Rq6Y1Apm9arVHia1glm9ao1suRpE6t+/Pw6Hg2XLlhEdHR34d3b8fj8Oh4M1a9bkW6iIiIiIyPnE7U4/zKJjx+5ERUUVds4ZmdQKZvWq1R4mtYJZvWqNbLkaRPrkk08AiI6ODvq3iIiIiIjYw+l00qJFEk5n+B9mYVIrmNWrVnuY1Apm9ao1suVqEOn0E2XrxNkiIiIiIvayLIu4uHKFnZErJrWCWb1qtYdJrWBWr1ojW56/nU1EREREROzjdruZPPlr3O7w/9Ygk1rBrF612sOkVjCrV62RLVd7Ig0fPvys79jhcPDcc8+d9c+JiIiIiAi4XE7atOmEyxX+h1mY1Apm9arVHia1glm9ao1suRpEmj9//lnf8ZlOvC0iIiIiImfmcFiUKlW6sDNyxaRWMKtXrfYwqRXM6lVrZMvVINLs2bPt7hARERERkUzcbjdTpnxDjx69w/5bg0xqBbN61WoPk1rBrF61RrY8nRNpx44dpKamZnt7amoqO3bsyHOUiIiIiMj5zuVy0aVLL1yuXP3dt1CZ1Apm9arVHia1glm9ao1seRpE6tChAzNnzsz29tmzZ9OhQ4c8R4mIiIiICLhc5vxl3KRWMKtXrfYwqRXM6lVr5MrTIJLf7z/j7W63G8vSF7+JiIiIiOSVx+NhypRv8Hg8hZ2SI5NawaxetdrDpFYwq1etkS3X+2ylpKRw5MiRwL8PHToU8pC1I0eO8N133xEfH58/hSIiIiIi5yGXy0WPHr2NOMzCpFYwq1et9jCpFczqVWtky/Uz9dFHHzFy5Egg/ZvXnnvuOZ577rmQ0/r9fh544IF8CRQREREROV95PG5jfrkxqRXM6lWrPUxqBbN61Rq5cv1MtW7dmmLFiuH3+/nf//5H9+7dqVevXtA0DoeDokWLUq9ePRo0aJDvsSIiIiIi5wuPx8P06RON+NYgk1rBrF612sOkVjCrV62RLdeDSI0bN6Zx48YAnDhxgiuvvJLatWvbFiYiIiIicj6LiorimmtuKOyMXDGpFczqVas9TGoFs3rVGtnydPbrwYMHawBJRERERMRGfr+PI0cO4/f7CjslRya1glm9arWHSa1gVq9aI1ueDvx76623cpzG4XBw77335uXuRURERETOex6Pl59+mkGXLlcTFRXe33xsUiuY1atWe5jUCmb1qjWy5fsgksPhwO/3axBJREREROQcREVFcdVVfQo7I1dMagWzetVqD5NawaxetUa2PA0irV27Nst1Pp+P7du388UXX/D777/z3nvvnXOciIiIiMj5yufzcfDgAWJjy2JZ4f0XcpNawaxetdrDpFYwq1etkS3fniXLsqhatSpDhw6levXqPPPMM/l11yIiIiIi5x2v18uCBcl4vd7CTsmRSa1gVq9a7WFSK5jVq9bIZstQW/Pmzfnpp5/suGsRERERkfNCVFQUXbtebcTXTpvUCmb1qtUeJrWCWb1qjWy2DCKtXLlSu4KJiIiIiJwDn8/H7t078fnC/1uDTGoFs3rVag+TWsGsXrVGtjyN9EyYMCHkf5988gn3338/33zzDZ07d87vVhERERGR84bP52XFisX4fOF/mIVJrWBWr1rtYVIrmNWr1siWpxNrDxs2LNvbYmNjGThwoL6ZTURERETkHLhcUXTs2L2wM3LFpFYwq1et9jCpFczqVWtky9Mg0qxZs7Jc53A4KFWqFCVKlDjnKBERERGR853P52Pnzm1UrFgl7E8VYVIrmNWrVnuY1Apm9ao1suXpWapcuXKW/ypVqqQBJBERERGRfOLz+diwYa0R5+owqRXM6lWrPUxqBbN61RrZ8rQnUoatW7fy888/s2PHDgAqVarEFVdcQdWqVfMlTkRERETkfOVyuWjbtlNhZ+SKSa1gVq9a7WFSK5jVq9bIludBpBdeeIFPPvkky4idZVnceuutDB069JzjRERERETOVz6fly1bNlOtWg0sy1nYOWdkUiuY1atWe5jUCmb1qjWy5elwtg8//JCPPvqIK6+8krFjx7Jw4UIWLlzI2LFj6dy5Mx999BEfffRRPqeKiIiIiJw/fD4/27dvwefzF3ZKjkxqBbN61WoPk1rBrF61RrY87Yn01Vdf0b59e15//fWg6xMTE3n11Vc5efIkY8aMYcCAAfnRKCIiIiJy3nG5XLRu3a6wM3LFpFYwq1et9jCpFczqVWtky9OeSNu3bycpKSnb25OSkti+fXueo0REREREznder5cNG9bi9XoLOyVHJrWCWb1qtYdJrfD/7d15fBNl4sfxbyZpC8h9iIAiq3IphyCCBwpiFwuCiBciirrueu6irrpeq6v7c70vvJZFkPViZUUOQS5RUUHE5ZBDQBBE5Cot5T7aTCa/P0oDpS1Nj2nmmX7er5cvQ5Kmn0kyM8nTeRKzemn1t1INItWrV08rV64s8vKVK1eqbt26pY4CAAAAKrtoNKqsrExFo96fZmFSq2RWL63uMKlVMquXVn8r1SBSWlqaxo4dq+HDh2vfvn2x8/ft26fhw4dr7Nix6t27d7lFAgAAAJVNKBRSly5dFQqV6QuVK4RJrZJZvbS6w6RWyaxeWv2tVINId955p84880y9+OKL6ty5s3r06KEePXqoc+fOevHFF3XmmWdqyJAh5d0KAAAAVBqRSEQrViw1YpqFSa2SWb20usOkVsmsXlr9rVTDbVWrVtXbb7+tmTNn6quvvtKmTZsk5X4WUrdu3dSjRw8FAoFyDQUAAAAql6j2798nyYRpFia1Smb10uoOk1ols3pp9bMyHbOVmpqq1NTU8moBAAAAfGnt2jV6663h+vHHlcrKylSVKlXUrNlJGjjwOnXten7sesuXL9OUKZO1fPkyrVmzWpFIRLNnzy9we1OmTNKTTz5e5O979NH/U8+evVxZlsIEgyF17Nilwn5fWZnUS6s7TGqVzOql1d+Y+AcAAAC4LD19s/bt26devS5W/foNdODAAX355ed64IE/6777HlK/fpdJkubOnaPJkyfo5JObq3HjJvr11/WKRCIKBoP5bq99+w565JG/F/g9Y8aM1po1q3XGGWdWyHLliUQi+uGHxTrttPYFWr3IpF5a3WFSq2RWL63+xiASAAAA4LKzz+6qs8/umu+8yy+/SjfddJ3GjHk/NojUv/8Vuvba65WSUkUvvPC0fv11faG316TJ8WrS5Ph852VnH9ALLzyjjh07qV69+u4sCACgUmMQCQAAAEiAYDCoY49tqJUrl8fOq1u3Xux03meMxvvX8dmzv9a+fXsrdBpbnmAwqHbtOlb47y0tk3ppdYdJrZJZvbT6W6m+nQ0AAABAye3fv187duzQxo0bNGbM+5o375sip55Fo7kf9BqJ2HHd9qefTlVKSoq6dbug3HrjFYnYWrhwXtytiWZSL63uMKlVMquXVn+L60ikd955R+edd55+85vfuN0DAAAA+NZrr72kiRPHSZIsy9L551+gu+/+SzE/Vfy3Hu/atVPz5s3Veed1V7Vqx5RDaUkFVLVqNcXT6g0m9dLqDpNaJbN6afWzuAaRnnrqKdWpUyc2iNS6dWs9++yz6tu3r6txAAAAgJ9cddU16t79QmVmZuiLL2bKcRzZdrjQ65ZkOtsXX3ymcDisnj3TyrU3XsFgUK1bt03I7y4Nk3ppdYdJrZJZvbT6W1zT2WrWrKlt27bF/p13aC0AAACA+J14YjOdeWYX9erVR88++7L279+n+++/u9DX146Te55tFz/N4tNPp6lmzVo666xzy705HrZta9682XG1eoFJvbS6w6RWyaxeWv0triORunTpoldffVUrVqxQjRo1JEkTJkzQ4sWLj/pzf/3rX8teCAAAAPhU9+4X6rnnntSvv/6ipk2b5bvs4IFIsSOSirJlyxYtXrxIl1zSX6FQYr43JxAIqG7d+sW2eoVJvbS6w6RWyaxeWv0trr3M3/72Nz355JOaM2eOtm3bpkAgoDlz5mjOnDlF/kwgEGAQCQAAADiK7OxsSdKePXsKXBbvdLaZM6cpGo0m5FvZ8gSDQTVv3iphv7+kTOql1R0mtUpm9dLqb3FNZ6tXr55eeOEFzZ49WytWrFA0GtVzzz2nlStXFvnfihUr3G4HAAAAjLB9e1aB82zb1rRpnyglJUXNmp1U4PJ4p7N9+ul0NWx4nNq1O71cWkvDtm3NmfOFMVNCTOql1R0mtUpm9dLqb6U63vWpp55Shw4dyrsFAAAA8KVnn31S+/btVfv2HdSgwbHati1Tn346Tb/8sk5//ONdqlatmiRpy5bNmjbtE0nSqlW5f5R9991RCgQCOu64RkpLuzjf7a5d+5PWrFmta6+9IaHTMSwroCZNmsqyzJgSYlIvre4wqVUyq5dWfyvVIFL//v1jp3/66Sdt3LhRktSkSROdcsop5VMGAAAAGMyyArE3Jj17XqSPP56gCRM+0s6dO3XMMdXUsmVr3XHHnTr//G6xn0lP36wRI4blu52RI/8lSerQ4Qz16ZP/25FnzpwuSUpL66VQqPBJBo4TjR3V5BbLCqpZs5Nd/R3lyaReWt1hUqtkVi+t/lbqT96bOXOmnn766dgAUp7jjz9eDzzwgC688MIyxwEAAAAmsqyAatWuplAwd2Dnqqsu01VXXVbsz6WmdtOPP/4Y9+95+OEH9PDDDxz1OnbE0c4d+1wdSLJtW7Nnf66uXXsk7MO9S8KkXlrdYVKrZFYvrf5Wqnvpyy+/1JAhQ9S4cWPdfffdOvnk3JG7NWvW6L///a/+9Kc/adiwYTr//PPLNRYAAAAwgWUFFApauvODRfppa8EPza4opxxbXUOv7iDLCrg6iGRZlpo3byXLiusjVxPOpF5a3WFSq2RWL63+VqpBpDfeeEMtW7bU+++/H5u/LUkXXnihrr32Wl1zzTV6/fXXGUQCAABApfbT1j36YdOuRGe4zrIsNWnSNNEZcTOpl1Z3mNQqmdVLq7+Varjtxx9/1KWXXppvAClPtWrV1L9//xIdhgsAAADAXLYd1syZn8i2w4lOiYtJvbS6w6RWyaxeWv2tVINIKSkp2rlzZ5GX79y5UykpKaWOAgAAAGAOywqqbduOsqxgolPiYlIvre4wqVUyq5dWfyvVIFKXLl30zjvvaNGiRQUuW7x4sd59912dffbZZY4DAAAA4H2WZalhw0bGfK6ISb20usOkVsmsXlr9rVT31H333aeUlBRdc801GjBggB544AE98MADGjBggK6++mqlpKTo3nvvLe9WAAAAAB4UDoc1deoEhcNmTAkxqZdWd5jUKpnVS6u/lWoQ6YQTTtDHH3+s6667Tjt37tSUKVM0ZcoU7dy5U4MHD9bEiRN1/PHHl3crAAAAAA8KBoPq3LmrgkEzpoSY1EurO0xqlczqpdXfSvXtbJJUr149PfTQQ3rooYfKswcAAACAYSzLUr169ROdETeTeml1h0mtklm9tPobE/8AAAAAlEk4HNakSR8aMyXEpF5a3WFSq2RWL63+xiASAAAAgDIJhYLq1q2nQiEzpoSY1EurO0xqlczqpdXfPDWItHr1ag0ZMkQXXnih2rdvry5dumjQoEH6/PPPC1x3zZo1uummm9ShQwd17txZ9913n7Kysgpcz3Ecvfnmm+rRo4fatm2rvn37avLkyRWxOAAAAEClEAhYqlmzlgIBT729KJJJvbS6w6RWyaxeWv3NU/fUpk2btHfvXvXv318PP/ywbr/9dknSbbfdpjFjxsSut2XLFg0aNEjr16/X3Xffrd/97nf68ssvdeONNyonJyffbb700kt6/vnnde655+qRRx5R48aNdc899+iTTz6p0GUDAAAA/CocDmv8+P8YMyXEpF5a3WFSq2RWL63+VuoP1nZDt27d1K1bt3znXXvttbrssss0atQoDRgwQJI0bNgw7d+/X+PGjVPjxo0lSe3atdONN96o8ePHx66Xnp6uUaNGadCgQXr00UclSVdeeaWuvfZaPfvss0pLS+NT2AEAAIAyCoVCSkvrp1DIU28vimRSL63uMKlVMquXVn8r8ZFI+/fv12WXXab//Oc/bvQUEAwG1ahRI+3evTt23owZM9S9e/fYAJIknXPOOWrWrJmmTp0aO2/mzJkKh8O65pprYucFAgENHDhQW7Zs0aJFiypkGQAAAAC/C4WSEp1QIib10uoOk1ols3pp9a8SDyJVrVpVGzZsUCAQcKNHkrRv3z5lZWVp/fr1+ve//62vvvpKZ511lqTco4u2bdumNm3aFPi5du3aacWKFbF/r1ixQtWqVdPJJ59c4Hp5l5dUJGIf/H9EkUgkdl7eadvOf9pxDj/tHDwdjp0Oh/OfjkYPPx1VNBotcFqSolEndtpx8p+27cNP2wdPR2TbkRIvr9uOtkxeU9zj5CW2HSn2ueclkYhTxPrkveeslLc+HdoWHDrtrfv20Ppf+DbCa4ra7uUth9cUtS33mrznZVH7Jy+JRJx861b+fa737t+i97neaz3a6wivOXy/Vdg+12uKfr3nrW3Xof2sO69hw+GwJk8eq/3797vwGrawfe6R24iSLVNOTo4mTx6rAwcOJOh1efzLlJ2drcmTx8q2bY+/18jtmzx5rLKzD5TL4+TmMh04sF+TJ49VTk5OhT73SrtM+/fn9uatb2V5nNxeprznQd5HzXhpfTpymfLWr9xtgbfWJ7cfp8KWKR6l+kyk8847T7Nnzy7Nj8bl6aef1tlnn63f/va3euaZZ5SamhqbjrZ161ZJUoMGDQr8XIMGDbRjx47YkzUjI0P16tUrMOCV97N5t1USS5d+L0n64YfF+uGHxZKkxYsXaNWq5ZKkBQu+1dq1qyVJ8+Z9rfXr10mSZs/+XJs3b5AkzZo1QxkZ6ZKkmTM/0fbtuR8IPm3ahNgRV7lP5P2xFdC27diGTpJ2796tadMmSJK2b8/SzJmfHFzmdM2aNUOStHnzBs2enfuh5OvXr9OyZd478ipvpZXyL9OuXbsSWFW4vKbCHqe8FdUrli1bVORzL+/55iVr164qdH1asWJpIrOKtH79Os2b97Ukae3a1Vqw4NuD569NZFYB33wzS1LR2wivKWq799VXMxOdVqiituVek/e8LGr/5CUbN66PrU+rVi3X4sULJOXuc9euXZXItEIVtc/dsmVTIrMKFYlEinwd4TXz5s0u8rVRdnZ2ItMKVdTrvbznslesWpX7x1O3XsPu379PffpcoenTJ5b7a9jC9rlHbiNKukwZGVvUp88VmjPn84S8Li/JMi1fvljNm7dWKBTy9HuNefO+VigU0qmntostR1kfJzeXadasGerT5wpt376tQp97pV2m6dMn6qKL+sm2w55bn45cplAopCpVqmrPnqLfP3llG/Hrrz+rT58rtHDht55bn9x+nI5cpryu4gSipfiTzpo1a3TnnXeqdevWGjBggE444QSlpKQUuF7t2rVLetOx209PT9fWrVs1depUJSUl6bHHHlP9+vU1f/58DRo0SC+99JJ69+6d7+eGDh2qN954Q//73/9Us2ZNXX/99crIyNCUKVPyXc9xHLVu3VqDBw/Www8/XKK29PTtsqxQbNAgGAweHA0MKBgMyrZtBQKHTltWQJaVd9qSZVmy7bAsKyjLshQOhxUMHjodCgUVCOSdzp2Xadt2vtNJSUmKRh3ZdkRJSUlynNyjOPJOO05EoVDeaUehUEiOE5FlWWrQoKYufuVr/bApsYM0pzWuqU+GnKesrD06cCCnwDJZllSvXg1PtW7btluOo0Ifp1DIUt261RPem9eakbFLjuMU+txLTk7y3H2bmblbth0psD5JUc89Z7dv36ucnLAcJ6pQKHdbEI3mng4EpPr1E3/fHnoe7JRkFbqNqFIlRXXqHOOZ1u3b92r//uxCt3uBQFQNGtRKeKuUv/fAgewC2/Lk5JAn7tfDWzMzdysaVaH7p+TkJE/0Ht4aDtuxdUuKKhjMPR0KBT21fuVuC+xC97le3IdlZe2RbTsFXkdUreqNbcHhrRkZO+U4KvS1UZUqyZ7a3+ZuC3IKfb0nOZ7Ydh1av3YpGg249ho2GLSUnZ2tYDCkpKTcqSHl9Rq2sH3ukduIki5TIBBQTk7uficYDFX46/KSLJNth5WdfUDVqlVXJBLx7HsNx4kqGAxq3749Sk6uoqSkpDI/Tm4uU+4RG7aSk1MUjToV9twr7TKFw2HZdlhVqlSNPR5eWZ+OXKZAIKA9e3apWrXqCgaDnlqfjlym3Ps2R6FQkoJBy1PrU0U/92w7rEaN6ha7XynVp0ddfPHFkqSffvpJkydPLvJ6pZkuJkknn3xybArapZdeqt/97ne69dZb9eGHH8YGq478FjZJsb9OValSJfb/eK5XEsFgSNGo8n0gdzB46G48/AO5ij59aM5l3k62pKcDAUtJSbkHkh16sXK000GFQp76Mj5JuZ9RVdQyeY1l5b4ZL/zxcG96Z2mEQkHZduDg6fzPPS/et8Ggpbzh7Pzrkzc/+D53o5t7Ov+2wFv3bSgUkm07RW4XvKao7Z5XnwdFbcu9Jhi0ZNtOkfskL8ndFoQOnj583Qp6bv2Sjr7/9ZpAIFDkPtdr8rZdUsHtgtf2t1J8r/28IPcNhePaa9hwOKxp0yaqT58rYo9Teb2GLXyfW9Tp+Jbp8N6831WRr8tLskzRqDRjxmT16XPFEftHb73XsKzcgY681qMvX+LfPwUCgdhzIO86FfHcK+0yBQIBTZ/+cb5er6xPRy5TOBzWzJlT1KfPFQoGg55an45cpvzbgmCRyxRPu1eWqfjTxbcfTan2bnfccUeF7sQvuugiPfroo/r555917LHHSsqdqnakjIwM1a5dW8nJyZJyp63NmzdP0Wg0X2/ez+bdFgAAAIDSS0pKUv/+AxOdETeTeml1h0mtklm9tPpbqQaR/vSnP5V3x1EdOJD7wWx79uzRSSedpLp162rZsmUFrrdkyRK1atUq9u/WrVvrww8/1Jo1a3TKKafEzl+8eHHscgAAAABlE4062r17t2rUqKFAwJtHuR3OpF5a3WFSq2RWL63+Vi730u7du8vlg4W3bdtW4LxwOKyJEyeqSpUqsSluPXv21KxZs7R58+bY9ebOnat169YpLS0tdt6FF16opKQkjR49OnZeNBrVBx98oIYNG6pDhw5lbgYAAAAqO9uO6MsvZ8ir36x6JJN6aXWHSa2SWb20+lupJ2svXbpUL7/8subPn69wOKyRI0fq7LPPVlZWlh5++GHdcMMN6tKlS4lu89FHH9WePXt05plnqmHDhsrIyNCkSZO0du1aPfDAAzrmmGMkSbfeequmTZumwYMHa/Dgwdq3b59GjhypFi1a6PLLL4/d3nHHHafBgwdr5MiRsm1bbdu21cyZMzV//nw9//zz+eYRAgAAACidpKQk9e17ZaIz4mZSL63uMKlVMquXVn8r1ZFICxcu1DXXXKNffvlFl1xyiRzHiV1Wt25d7dmzR2PGjCnx7fbu3VuWZek///mPHnvsMf373//WcccdpzfeeEM33nhj7HqNGjXSe++9p6ZNm+qFF17QiBEjdP7552vUqFGxz0PKc++99+rPf/6zZs+erccff1wbN27Uc889p759+5Zm0QEAAAAcwXEcbduWme99gZeZ1EurO0xqlczqpdXfSnUk0ksvvaSTTz5Z//3vf7Vnzx59+OGH+S7v0qWLxo8fX+Lbvfjii2Pf/Fac5s2ba+TIkcVez7Is3XLLLbrllltK3AMAAACgeJFIRN99N1upqRd78hsKj2RSL63uMKlVMquXVn8r1SDS0qVL9ec//1nJycmFfktbw4YNlZmZWeY4AAAAAN6XlJSkXr0uTXRG3EzqpdUdJrVKZvXS6m+lGmoLhUJHPdwrPT1d1apVK3UUAAAAAHM4jqP09M3GTAkxqZdWd5jUKpnVS6u/lWoQqX379po+fXqhl+3bt0/jxo3TmWeeWaYwAAAAAGZwnIiWLl0oxzHjG45M6qXVHSa1Smb10upvpZrONmTIEF177bW6+eabY59h9OOPP2rDhg0aOXKksrKydPvtt5drKAAAAABvCoWSlJoa32ebeoFJvbS6w6RWyaxeWv2t1EciDR8+XL/88ovuv/9+SdLTTz+tRx55RI7jaPjw4WrVqlW5hgIAAADwJsdxtHHjemOmhJjUS6s7TGqVzOql1d9KdSSSJJ199tmaPn26li9frl9++UXRaFQnnHCC2rRpU+iHbQMAAADwJ8dxtHr1SjVs2NiIbzgyqZdWd5jUKpnVS6u/lXoQKc+pp56qU089tTxaAAAAABgoFAqpe/eeic6Im0m9tLrDpFbJrF5a/a3UQ205OTl677339Ic//EG9e/dW79699Yc//EHvvfeesrOzy7MRAAAAgIc5TkTr1q0x5sNpTeql1R0mtUpm9dLqb6UaRNqyZYv69eunJ554QitXrlTdunVVt25drVy5Uk888YT69eunLVu2lHcrAAAAAA9ynOjBzxWJJjolLib10uoOk1ols3pp9bdSTWd7/PHHtWnTJr388stKS0vLd9nUqVP1wAMP6PHHH9c///nPcokEAAAA4F2hUEjnnntBojPiZlIvre4wqVUyq5dWfyvVkUjffvutbrjhhgIDSJLUq1cvDR48WN9++22Z4wAAAAB4XyQS0erVKxWJmDElxKReWt1hUqtkVi+t/laqQaRjjjlGdevWLfLy+vXr65hjjil1FAAAAABzRKNRZWVlKho1Y0qISb20usOkVsmsXlr9rVSDSJdddpnGjx+v/fv3F7hs7969GjdunC6//PIyxwEAAADwvlAopC5duioUKvOXP1cIk3ppdYdJrZJZvbT6W1z31IwZM/L9u3Xr1po1a5Z69eqlSy+9VCeeeKIkad26dZo4caJq1aqlli1bln8tAAAAAM+JRCJatWq5WrQ4VcFgMNE5xTKpl1Z3mNQqmdVLq7/FNYg0ZMgQBQKB2CFeh58eNmxYgetv2bJF99xzj3r37l2OqQAAAAC8Kar9+/dJMmVKiEm9tLrDpFbJrF5a/SyuQaR33nnH7Q4AAAAAhgoGQ+rYsUuiM+JmUi+t7jCpVTKrl1Z/i2sQqXPnzm53AAAAADBUJBLRDz8s1mmntTdiSohJvbS6w6RWyaxeWv2tVB+sDQAAAAAAgMql1B9BPn/+fH300UfasGGDdu7cWeAr8QKBgD7++OMyBwIAAADwtmAwqHbtOiY6I24m9dLqDpNaJbN6afW3Uh2JNGrUKF133XWaOnWq9uzZo1q1aql27dr5/qtVq1Z5twIAAADwoEjE1sKF8xSJ2IlOiYtJvbS6w6RWyaxeWv2tVEcijRw5Uh07dtSwYcNUo0aN8m4CAAAAYJSAqlatJimQ6JA4mdRLqztMapXM6qXVz0o1iLR//3717duXASQAAAAACgaDat26baIz4mZSL63uMKlVMquXVn8r1XS2Ll26aNWqVeXdAgAAAMBAtm1r3rzZsm0zpoSY1EurO0xqlczqpdXfSjWI9Mgjj2ju3LkaOXKkduzYUc5JAAAAAEwSCARUt259BQJmTAkxqZdWd5jUKpnVS6u/lWo6W6NGjTRgwAA9++yzev7555WSkiLLyj8eFQgEtGDBgnKJBAAAAOBdwWBQzZu3SnRG3EzqpdUdJrVKZvXS6m+lGkQaOnSohg0bpoYNG6pNmzZ8NhIAAABQieVOCflaXbqcp1CoVG8xKpRJvbS6w6RWyaxeWv2tVPfSBx98oG7duumNN94ocAQSAAAAgMrFsgJq0qSpLMuMKSEm9dLqDpNaJbN6afW3Ug0ihcNhde/enQEkAAAAALKsoJo1OznRGXEzqZdWd5jUKpnVS6u/lWoUqHv37po/f355twAAAAAwkG3bmjVrhjHfcGRSL63uMKlVMquXVn8r1SDSH//4R61Zs0aPPfaYli1bpqysLO3YsaPAfwAAAAD8z7IsNW/eypiZCib10uoOk1ols3pp9bdSTWdLS0uTJK1YsUJjxowp8norVqwoXRUAAAAAY1iWpSZNmiY6I24m9dLqDpNaJbN6afW3Ug0i3XHHHQoE+OApAAAAAJJthzVr1gx1795ToVBSonOKZVIvre4wqVUyq5dWfyvVINKf/vSn8u4AAAAAYCjLCqpt246yrGCiU+JiUi+t7jCpVTKrl1Z/K9UgEgAAAADksSxLDRs2SnRG3EzqpdUdJrVKZvXS6m+lGkR67bXXir1OIBDQHXfcUZqbBwAAAGCQcDismTM/UWrqxUpK8v6UEJN6aXWHSa2SWb20+lu5DyIFAgFFo1EGkQAAAIBKIhgMqnPnrgoGzZgSYlIvre4wqVUyq5dWfyvVINLKlSsLnOc4jjZu3KjRo0frf//7n958880yxwEAAADwPsuyVK9e/URnxM2kXlrdYVKrZFYvrf5mldsNWZZOOOEE3X///TrxxBP1xBNPlNdNAwAAAPCwcDisSZM+VDgcTnRKXEzqpdUdJrVKZvXS6m/lNoh0uDPPPFNffvmlGzcNAAAAwGNCoaC6deupUMiMKSEm9dLqDpNaJbN6afU3V76dbdmyZbIsV8anAAAAAHhMIGCpZs1aic6Im0m9tLrDpFbJrF5a/a1UIz0TJkwo9L933nlHQ4YM0dixY3XRRReVdysAAAAADwqHwxo//j/GTAkxqZdWd5jUKpnVS6u/lepIpAceeKDIy+rUqaObb76Zb2YDAAAAKolQKKS0tH4KhVyZ6FDuTOql1R0mtUpm9dLqb6W6pz777LMC5wUCAdWsWVPVq1cvcxQAAAAAs4RCSYlOKBGTeml1h0mtklm9tPpXqaazNWnSpMB/jRs3ZgAJAAAAqIRs29bkyWNl23aiU+JiUi+t7jCpVTKrl1Z/49OvAQAAAJRJKBRSnz5XGDMlxKReWt1hUqtkVi+t/hb3PdW3b98S3XAgENDHH39c4iAAAAAA5rHtsFFvxEzqpdUdJrVKZvXS6l9xH4lUu3btuP6zbVurV6/W6tWr3ewGAAAA4BG2bWvatInGTAkxqZdWd5jUKpnVS6u/xT3c9u677x718oyMDL355psaM2aMgsGgLrnkkjLHAQAAAPC+pKQk9e8/MNEZcTOpl1Z3mNQqmdVLq7+V+ZitzMxMDR8+XP/9739l27b69u2r2267TU2bNi2PPgAAAAAeF4062r17t2rUqKFAwPsfu2pSL63uMKlVMquXVn8r9b2UkZGhJ598UqmpqRo9erR69eqlqVOn6qmnnmIACQAAAKhEbDuiL7+cIduOJDolLib10uoOk1ols3pp9bcSH4mUkZGh4cOH68MPP5Rt27rkkkt022236YQTTnCjDwAAAIDHJSUlqW/fKxOdETeTeml1h0mtklm9tPpb3INIW7dujQ0eRSIR9evXT7feeiuDRwAAAEAl5ziOtm/PUp06dWVZ3p8SYlIvre4wqVUyq5dWf4v7Xvrtb3+r999/XyeffLJeeOEFXXPNNdq1a5d++OGHIv8DAAAA4H+RSETffTdbkYgZU0JM6qXVHSa1Smb10upvcR+JlJ2dLUlavny57rrrrqNeNxqNKhAIaMWKFWWKAwAAAOB9SUlJ6tXr0kRnxM2kXlrdYVKrZFYvrf4W9yDSU0895WYHAAAAAEM5jqOMjHQ1aNDQiCkhJvXS6g6TWiWzemn1t7gHkfr37+9mBwAAAABDOU5ES5cuVPfuPY14I2ZSL63uMKlVMquXVn8r8bezAQAAAMDhQqEkpaZenOiMuJnUS6s7TGqVzOql1d8YagMAAABQJo7jaOPG9XIcJ9EpcTGpl1Z3mNQqmdVLq79xJBIAAACAmBUrftDUqZO1cOECbdmySTVr1tJpp7XVH/5wm5o2PTF2veXLl2nKlMlavnyZ1qxZrUgkolmzvi10SkhW1jYNG/aavvlmtvbt26dmzZrp2mtvVI8eqRW5aDGO42j16pVq2LCx56ew0OoOk1ols3pp9TfuJQAAAAAx77//tr788nN16nSm7rzzXl1ySX8tXrxIN910rdau/Sl2vblz52jy5AkKBAJq3LiJJCkUKvg36r179+j223+vWbM+V79+l+mOO+5UtWrH6NFHH9CMGdMqbLkOFwqF1L17z0J7vYZWd5jUKpnVS6u/MYgEAAAAIGbAgEEaO3ay7rrrPvXte6luuOH3ev31NxWJRPTee2/Hrte//xWaPn2WRo58V506dZaU+yG1R5o4cZw2bPhVTz31vP7wh9t0+eVX6ZVXhql161P1+usvKRwOV9iy5XGciNatW1Nor9fQ6g6TWiWzemn1NwaRAAAAAMS0bdteSUlJ+c474YSmatbsJK1b93PsvLp16yklpYokKRrNPc9xogVub/HiRapdu47OOOPM2HmWZemCC36rbdu26fvvF7iwFEfnONGDn4NSsNdraHWHSa2SWb20+huDSAAAAACOKhqNavv2LNWuXbvQyy0rIKnw6WzhcFgpKSkFzq9SJXcAauXKleUXGqdQKKRzz73AiCkstLrDpFbJrF5a/Y1BJAAAAABHNWPGVGVkbFWPHr8t9PLowUORIpGCU0KaNj1RGRlbtWXL5nznL168SJKUmbm1nGuLF4lEtHr1ykJ7vYZWd5jUKpnVS6u/MYgEAAAAoEi//LJOL774jNq0aadevfoUep286Wx5g0mH69PnUlmWpUceeUBLly7Wxo0b9O67o/T117MkSdnZ2e6EH0U0GlVWVmahvV5DqztMapXM6qXV3xhEAgAAAFCobdsydd99d+qYY6rriSeeUTAYLPR6R5vOdsopzfW3vz2hTZs26LbbbtKAAZdq7NgPNGTInyVJVatWc28BihAKhdSlS1cjprDQ6g6TWiWzemn1NwaRAAAAABSwZ88e3XvvEO3Zs0cvvPCq6tdvUOR1jzadTZIuuCBVEyZM05tvvq1hw0Zp7NjJatz4eEm5H9pd0SKRiFasWGrEFBZa3WFSq2RWL63+xnAbAAAAgHyys7N1//1369df1+vll9/Qb35zUpw/WfSUkKSkJLVufVrs3/Pnz5MkderUuSyppRTV/v37dLRe76DVHSa1Smb10upnDCIBAAAAiIlEIvrb3x7UsmVL9PTTL6hNm3bF/kwgkDudLRiM7+3Fr7+u14QJ43TOOeepadMTy9RbGsFgSB07dqnw31satLrDpFbJrF5a/Y1BJAAAAAAxr732smbP/krnnnuedu3apenTp+S7/KKLekuStmzZrGnTPpEkrVy5XJI0atSbCgQCOu64RkpLuzj2M9dee6UuuCBVDRs21KZNmzRhwkeqWbOm7rvvwQpaqvwikYh++GGxTjutfZGf8+QVtLrDpFbJrF5a/Y1BJAAAAKCSs6xA7MOxf/pplSRpzpyvNWfO1wWue/HFud/Qlp6+WSNGDMt32ciR/5Ikdehwhvr06Rs7v3nzFpoyZZKysrapdu3auvDC3+oPf7hVdevWLXD7jhOV4zC1BAC8iEEkAAAAoBKzrIBq1a6mUDD3O3c++GB0XD+XmtpNP/74Y1zXfe21V+LusSOOdu7Y5+pAUjAYVLt2HV27/fJEqztMapXM6qXV3xhEAgAAACoxywooFLR05weL9NPWPQltOeXY6hp6dQdZVsDVQaRIxNbixQvUvv0ZcX+OU6LQ6g6TWiWzemn1N+4lAAAAAPpp6x79sGlXojMqSEBVq1aTFEh0SBxodYdJrZJZvbT6GYNIAAAAACqVYDCo1q3bJjojLrS6w6RWyaxeWv3NSnQAAAAAAFQk27Y1b95s2bad6JRi0eoOk1ols3pp9TcGkQAAAABUKoFAQHXr1lcg4P0pLLS6w6RWyaxeWv2N6WwAAAAAKpVgMKjmzVslOiMutLrDpFbJrF5a/Y0jkQAAAABUKrZta86cL4yYwkKrO0xqlczqpdXfGEQCAAAAUKlYVkBNmjSVZXl/Cgut7jCpVTKrl1Z/YzobAAAAgErFsoJq1uzkRGfEhVZ3mNQqmdVLq79xJBIAAACASsW2bc2aNcOIKSy0usOkVsmsXlr9jUEkAAAAAJWKZVlq3ryVLMv7b4dodYdJrZJZvbT6G9PZAAAAAFQqlmWpSZOmic6IC63uMKlVMquXVn/z1HDbkiVL9Pe//10XX3yxTj/9dHXv3l133nmnfv755wLXXbNmjW666SZ16NBBnTt31n333aesrKwC13McR2+++aZ69Oihtm3bqm/fvpo8eXJFLA4AAAAAD7LtsGbO/ES2HU50SrFodYdJrZJZvbT6m6eORBoxYoQWLlyotLQ0tWzZUhkZGXr//fd12WWXacyYMWrRooUkacuWLRo0aJBq1Kihu+++W/v27dNbb72lVatW6cMPP1RycnLsNl966SUNHz5cV111ldq2bavPPvtM99xzjwKBgC6++OJELSoAAACABLGsoNq27SjLCiY6pVi0usOkVsmsXlr9zVODSDfccIOef/75fINAvXv3Vt++fTV8+HA9//zzkqRhw4Zp//79GjdunBo3bixJateunW688UaNHz9eAwYMkCSlp6dr1KhRGjRokB599FFJ0pVXXqlrr71Wzz77rNLS0hQM8mQBAAAAKhPLstSwYaNEZ8SFVneY1CqZ1Uurv3lqOlvHjh3zDSBJUrNmzdS8eXOtXbs2dt6MGTPUvXv32ACSJJ1zzjlq1qyZpk6dGjtv5syZCofDuuaaa2LnBQIBDRw4UFu2bNGiRYtcXBoAAAAAXhQOhzV16gSFw96fwkKrO0xqlczqpdXfPDWIVJhoNKrMzEzVqVNHUu7RRdu2bVObNm0KXLddu3ZasWJF7N8rVqxQtWrVdPLJJxe4Xt7lJRWJ2Af/H1EkEomdl3fatvOfdpzDTzsHT4djp8Ph/Kej0cNPRxWNRguczr1fnNhpx8l/Om8+Z+5p++DpiGw7UuLlddvRlslrinucvMS2I8U+97wkEnGKWJ+895yV8tanQ9uCQ6e9dd8eWv8L30Z4TVHbPa9+5WpR23KvyXteFrV/8pJIxMm3buXf53rv/i16n+u91qO9jvCaw/dbhe1zvabo13ve2nYd2s8W/hrWa/JeAxS9zz1yG1Gy1+WBQECdO3dVNOok6HV5/MskRdWp0zkKBoMef69hKxgMqlOnc2LralkfJzeXyXEcde7cVYFAoFweJ7eXyXEcnXnmubIsq8Kee6VdpmAwqI4dz1IgECjz4+T2Mkk6uC2Iem59cvtxKmyZ4uH5QaSPP/5Y6enp6tWrlyRp69atkqQGDRoUuG6DBg20Y8cO5eTkSJIyMjJUr1692JP38OsdflslsXTp95KkH35YrB9+WCxJWrx4gVatWi5JWrDgW61du1qSNG/e11q/fp0kafbsz7V58wZJ0qxZM5SRkS5JmjnzE23fnvuB4NOmTdDu3bslSZMnj9WBA/tl27YmTx4r27Z14MB+TZ48VpK0e/duTZs2QZK0fXuWZs785OAyp2vWrBmSpM2bN2j27M8lSevXr9OyZd478io7O7vQZdq1a1cCqwqX11TY4+S1F2DLli0q8rmX93zzkrVrVxW6Pq1YsTSRWUVav36d5s37WpK0du1qLVjw7cHz1x7txyrcN9/MklT0NsJritruffXVzESnFaqobbnX5D0vi9o/ecnGjetj69OqVcu1ePECSbn73LVrVyUyrVBF7XO3bNmUyKxCRSKRIl9HeM28ebOLfG2UnZ2dyLRCFfV6L++57BWrVuX+8bSo17Be8/33/5NU9D73yG1ESV+Xp6dvUr169fXVVzMT8rq8JMu0dOkiZWamy7IsT7/XmDfva1mWpR07srRo0Xfl8ji5uUyffz5V9erV17ZtGeXyOLm9TFOmjFOtWrWVk1P4+yc3nnulXSbLsrRw4bfauXNHmR8nt5dp3bo1qlevvv73vzmeW5/cfpyOXKa8ruIEol78k85Ba9as0VVXXaXmzZvr/fffVzAY1Pz58zVo0CC99NJL6t27d77rDx06VG+88Yb+97//qWbNmrr++uuVkZGhKVOm5Lue4zhq3bq1Bg8erIcffrhETenp22VZodigQTAYPDgaGIj9dSAQOHTasgKyrLzTlizLkm2HZVnB2ChyMHjodCgUVCCQdzr3I6ts2853OikpSdGoI9uOKCkpSY6TexRH3mnHiSgUyjvtKBQKyXEisixLDRrU1MWvfK0fNiV2kOa0xjX1yZDzlJW1RwcO5BRYJsuS6tWr4anWbdt2y3FU6OMUClmqW7d6wnvzWjMydslxnEKfe8nJSZ67bzMzd8u2IwXWJynquefs9u17lZMTluNEFQrlbgui0dzTgYBUv37i79tDz4OdkqxCtxFVqqSoTp1jPNO6ffte7d+fXeh2LxCIqkGDWglvlfL3HjiQXWBbnpwc8sT9enhrZuZuRaMqdP+UnJzkid7DW8NhO7ZuSVEFg7mnQ6Ggp9av3G2BXeg+14v7sKysPbJtp8DriKpVvbEtOLw1I2OnHEeFvjaqUiXZU/vb3G1BTqGv9yTHE9uuQ+vXLkWjgUJfw6akeGNbcHhvRsYuSQE5TqTQfe6R2wipZK/LI5GIZsz4WKmpfZSSklLhr8tLskwHDhzQjBkfq1ev/goEAp59r+E4uUdfTJ06Xj179lWVKlXL/Di5uUzZ2Qc0c+Yn6tnzEgWDVpkfJ7eXaf/+/Zo5c7LS0i5VIBCokOdeaZcpEolo6tTxuuiifkpJSfHU+nTkMtm2rU8/nXRwW5DsqfWpop97th1Wo0Z1i91Oe+qDtQ+XkZGhW265RTVq1NDQoUNjH4CdkpIiSbGjjQ6X99epKlWqxP4fz/VKIhgMKRpVvg/kDgYP3Y15T5ijn06KnU5KKt3pQMBSUlLugWSHXqwc7XRQoZD3DjzL2wDmns6/TF5jWblvxgt/PAJF/VhChEJB2Xbg4On8zz0v3rfBoKW84ez865M3P/g+d6Obezr/tsBb920oFJJtO0VuF7ymqO2eV58HRW3LvSYYtGTbTpH7JC/J3RaEDp4+fN0Kem79ko6+//WavDeehe1zvSZv2yUV3C54bX8rxffazwty31A4Rb6G9Zrc1zLOUfa5RZ2O73V5MGipW7eeqlIlRYFA7i+oyNflJVmmlJRkde9+UewNa/HLl5j3GpaVO22ne/eLYu/Zyvo4ublMVapUUbduPZWcnBS7X8vyOLm9TFWrVlH37hcpKSkU63X7uVfaZQoEpO7dL1JyclLcy1dR69ORy1TYtsAr61Npl6n408W3H40nXz3s3r1bf/jDH7R7926NGDFCDRs2jF127LHHSsodZDpSRkaGateuHftw7gYNGigzM7PA/Pm8n827LQAAAACVRyBgqWbNWvkGZbyKVneY1CqZ1Uurv3nunsrOztatt96qdevWadiwYTrllFPyXd6wYUPVrVtXy5YtK/CzS5YsUatWrWL/bt26tfbv3681a9bku97ixYtjlwMAAACoXMLhsMaP/49nP2D+cLS6w6RWyaxeWv3NU4NIkUhEd911l77//nsNHTpUHTp0KPR6PXv21KxZs7R58+bYeXPnztW6deuUlpYWO+/CCy9UUlKSRo8eHTsvGo3qgw8+UMOGDYu8fQAAAAD+FQqFlJbWz3PTDgtDqztMapXM6qXV3zx1Tz399NP6/PPPdcEFF2jHjh2aOHFivsv79esnSbr11ls1bdo0DR48WIMHD9a+ffs0cuRItWjRQpdffnns+scdd5wGDx6skSNHyrZttW3bVjNnztT8+fP1/PPP55tHCAAAAKDyOPxzQbyOVneY1CqZ1Uurf3nqSKSVK1dKkr744gv95S9/KfBfnkaNGum9995T06ZN9cILL2jEiBE6//zzNWrUqNjnIeW599579ec//1mzZ8/W448/ro0bN+q5555T3759K3TZAAAAAHjD4V/F7XW0usOkVsmsXlr9zVNHIr377rtxX7d58+YaOXJksdezLEu33HKLbrnllrKkAQAAAPCJUCikPn2uMGIKC63uMKlVMquXVn/z1JFIAAAAAFARbNucD9Kl1R0mtUpm9dLqXwwiAQAAAKhUbNvWtGkTjZjCQqs7TGqVzOql1d84ZgsAAABApZKUlKT+/QcmOiMutLrDpFbJrF5a/Y0jkQAAAABUKtGoo127dioadRKdUixa3WFSq2RWL63+xiASAAAAgErFtiP68ssZsu1IolOKRas7TGqVzOql1d+YzgYAAACgUklKSlLfvlcmOiMutLrDpFbJrF5a/Y0jkQAAAABUKo7jaNu2TDmO96ew0OoOk1ols3pp9TcGkQAAAABUKpFIRN99N1uRiPensNDqDpNaJbN6afU3prMBAAAAqFSSkpLUq9elic6IC63uMKlVMquXVn/jSCQAAAAAlYrjOEpP32zEFBZa3WFSq2RWL63+xiASAAAAgErFcSJaunShHMf7U1hodYdJrZJZvbT6G9PZAAAAAFQqoVCSUlMvTnRGXGh1h0mtklm9tPobRyIBAAAAqFQcx9HGjeuNmMJCqztMapXM6qXV3xhEAgAAAFCpOI6j1atXGvHGkVZ3mNQqmdVLq78xnQ0AAABApRIKhdS9e89EZ8SFVneY1CqZ1Uurv3EkEgAAAIBKxXEiWrdujREfpkurO0xqlczqpdXfGEQCAAAAUKk4TvTg56BEE51SLFrdYVKrZFYvrf7GdDYAAAAAlUooFNK5516Q6Iy40OoOk1ols3pp9TeORAIAAABQqUQiEa1evVKRiPensNDqDpNaJbN6afU3BpEAAAAAVCrRaFRZWZmKRr0/hYVWd5jUKpnVS6u/MZ0NAAAAQKUSCoXUpUvXRGfEhVZ3mNQqmdVLq79xJBIAAACASiUSiWjFiqVGTGGh1R0mtUpm9dLqbwwiAQAAAKhkotq/f58kE6aw0OoOk1ols3pp9TOmswEAAACoVILBkDp27JLojLjQ6g6TWiWzemn1N45EAgAAAFCpRCIRLVmy0IgpLLS6w6RWyaxeWv2NQSQAAAAAAAAUi+lsAAAAACqVYDCodu06JjojLrS6w6RWyaxeWv2NI5EAAAAAVCqRiK2FC+cpErETnVIsWt1hUqtkVi+t/sYgEgAAAIBKJqCqVatJCiQ6JA60usOkVsmsXlr9jOlsAAAAACqVYDCo1q3bJjojLrS6w6RWyaxeWv2NI5EAAAAAVCq2bWvevNmybe9PYaHVHSa1Smb10upvDCIBAAAAqFQCgYDq1q2vQMD7U1hodYdJrZJZvbT6G9PZAAAAAFQqwWBQzZu3SnRGXGh1h0mtklm9tPobRyIBAAAAqFRs29acOV8YMYWFVneY1CqZ1UurvzGIBAAAAKBSsayAmjRpKsvy/hQWWt1hUqtkVi+t/sZ0NgAAAACVimUF1azZyYnOiAut7jCpVTKrl1Z/40gkAAAAAJWKbduaNWuGEVNYaHWHSa2SWb20+huDSAAAAAAqFcuy1Lx5K1mW998O0eoOk1ols3pp9TemswEAAACoVCzLUpMmTROdERda3WFSq2RWL63+xnAbAAAAgErFtsOaOfMT2XY40SnFotUdJrVKZvXS6m8MIgEAAACoVCwrqLZtO8qygolOKRat7jCpVTKrl1Z/YzobAAAAgErFsiw1bNgo0RlxodUdJrVKZvXS6m8ciQQAAACgUgmHw5o6dYLCYe9PYaHVHSa1Smb10upvDCIBAAAAqFSCwaA6d+6qYND7U1hodYdJrZJZvbT6G9PZAAAAAFQqlmWpXr36ic6IC63uMKlVMquXVn/jSCQAAAAAlUo4HNakSR8aMYWFVneY1CqZ1UurvzGIBAAAAKBSCYWC6tatp0Ih709hodUdJrVKZvXS6m9MZwMAAABQqQQClmrWrJXojLjQ6g6TWiWzemn1N45EAgAAAFCphMNhjR//HyOmsNDqDpNaJbN6afU3BpEAAAAAVCqhUEhpaf0UCnl/Ygat7jCpVTKrl1Z/YxAJAAAAQKUTCiUlOiFutLrDpFbJrF5a/YtBJAAAAACVim3bmjx5rGzbTnRKsWh1h0mtklm9tPobg0gAAAAAKpVQKKQ+fa4wYgoLre4wqVUyq5dWf2MQCQAAAEClY9vmfJAure4wqVUyq5dW/2IQCQAAAEClYtu2pk2baMQUFlrdYVKrZFYvrf7GMVsAAAAAKpWkpCT17z8w0RlxodUdJrVKZvXS6m8ciQQAAACgUolGHe3atVPRqJPolGLR6g6TWiWzemn1NwaRAAAAAFQqth3Rl1/OkG1HEp1SLFrdYVKrZFYvrf7GdDYAAAAAlUpSUpL69r0y0RlxodUdJrVKZvXS6m8ciQQAAACgUnEcR9u2ZcpxvD+FhVZ3mNQqmdVLq78xiAQAAACgUolEIvruu9mKRLw/hYVWd5jUKpnVS6u/MZ0NAAAAgLH27dun//znXf3wwzKtWPGDdu/epYce+pt69+6b73rLly/TlCmTtXz5Mq1Zs1qRSES9el1a5O1mZW3TiBHD9M03s7Vr107VrVtPZ5xxph588FGXlyi/pKSko3Z6Ca3uMamXVn/jSCQAAAAAxtq5c4dGjXpTv/zys045pXmR15s7d44mT56gQCCgxo2bSFKRU1jS07fo978frG+//UaXXnq5/vzn+9WnTz/t2LHdlWU4GsdxlJ6+2YjpNrS6x6ReWv2NQSQAAAAAxqpXr74mTpymjz6arDvuuLPI6/Xvf4WmT5+lkSPf1RlnnClJcpzCp7A899yTCgZDGjXqfd1ww+/Vp08/3XDD7/XMMy+5sgxH4zgRLV26sMhWL6HVPSb10upvTGcDAAAAYKzk5GTVq1e/2OvVrVsvdtqycv+WHgolFbjeL7+s07fffqN77nlAtWrVVnZ2toLBoEKhxLx1CoWSlJp6cUJ+d0nR6h6Temn1N45EAgAAAFCpRKO5/y9sCsv8+fMkSXXr1tWdd96mCy88VxdeeK7uuWeINm/eVJGZknIbN25cb8R0G1rdY1Ivrf7GIBIAAACASiV6cBSpsDeOv/76qyTp2Wf/oVAoSY8//pRuueWPWrLke9111+06cOBAhbY6jqPVq1ca8SaXVveY1EurvzGdDQAAAEClYlkBSSp0itr+/fsk5U5/e+65l2NT34499lg99tjD+vTTaerb99IKaw2FQurevWeF/b6yoNU9JvXS6m8ciQQAAACgUjl0JFLBD9NNSUmRJPXo8dvYAJIkXXBBqoLBoJYuXVwxkQc5TkTr1q0x4oN/aXWPSb20+huDSAAAAAAqlUOfiRQtcFn9+g0kSXXq1M13fjAYVK1atbV7927X+w7nONGDn9lSsNVraHWPSb20+hvT2QAAAABUKkebztayZWtJUmZmRr7zw+Gwdu7codq167gfeJhQKKRzz72gQn9nadHqHpN6afU3jkQCAAAAUKnkTWeLRApOYenQ4QzVqVNXM2ZMVXZ2duz8KVMmKRKJ6Mwzu1RYp5TbuHr1ykJbvYZW95jUS6u/cSQSAAAAAGNYViB2JFGeDz/8QLt374kdPfTNN1/HTl911QBVr15Dmzdv0tSpUyRJP/64UpL07rujZFmWGjU6Tr169ZEkhUJV9Kc/3aW///1R/elPNyst7WKlp2/WmDH/0emnd9CFF16oYDD3b/GOE3V9Gkw0GlVWVqZ+85tTXP095YFW95jUS6u/MYgEAAAAwAiWFVCt2tUUCuafUPHBB+9r48aNsX/PmvW5Zs36XJI0YMDlqlPnGK1alaXhw9/I93MjRgyTJHXu3FnXXDMgdv6gQQNUu3Z1DR8+XK+99rJq1qypq6++WnfffbeqV68eu54dcbRzxz5XB5JCoZC6dOnq2u2XJ1rdY1Ivrf7GIBIAAAAAI1hWQKGgpTs/WKSftu6JnV/10sdV1HEEt4z7WdLPkqRT7hhR6HWyJF38ytdHnFtTuvBeNb0w91/fShrw1qLYpaccW11Dr+4gywq4OogUiUS0atVytWhxqoLBoGu/pzzQ6h6Temn1NwaRAAAAABjlp6179MOmXYnOqCBR7d+/T5IJ3x5Fq3tM6qXVzxhEAgAAAACPCgZD6tixYj/Mu7RodY9JvbT6G9/OBgAAAAAeFYlEtGTJQiO+PYpW95jUS6u/MYgEAAAAAACAYjGdDQAAAAA8KhgMql27jonOiAut7jGpl1Z/40gkAAAAAPCoSMTWwoXzFInYiU4pFq3uMamXVn9jEAkAAAAAPCugqlWrSQokOiQOtLrHpF5a/YzpbAAAAADgUcFgUK1bt010RlxodY9JvbT6G0ciAQAAAIBH2batefNmy7a9P92GVveY1Eurv3lqEGnv3r165ZVXdNNNN6lz585q2bKlxo0bV+h116xZo5tuukkdOnRQ586ddd999ykrK6vA9RzH0ZtvvqkePXqobdu26tu3ryZPnuz2ogAAAABAmQUCAdWtW1+BgPen29DqHpN6afU3T01n2759u15//XU1btxYLVu21HfffVfo9bZs2aJBgwapRo0auvvuu7Vv3z699dZbWrVqlT788EMlJyfHrvvSSy9p+PDhuuqqq9S2bVt99tlnuueeexQIBHTxxRdX1KIBAAAAQIkFg0E1b94q0RlxodU9JvXS6m+eOhLp2GOP1ezZs/XFF1/oL3/5S5HXGzZsmPbv36+3335bgwcP1q233qqXX35ZK1eu1Pjx42PXS09P16hRozRo0CD93//9n6666ioNGzZMnTp10rPPPqtIJFIRiwUAAAAApWLbtubM+cKI6Ta0usekXlr9zVODSMnJyWrQoEGx15sxY4a6d++uxo0bx84755xz1KxZM02dOjV23syZMxUOh3XNNdfEzgsEAho4cKC2bNmiRYsWle8CAAAAAEA5sqyAmjRpKsvy/nQbWt1jUi+t/uapQaR4pKena9u2bWrTpk2By9q1a6cVK1bE/r1ixQpVq1ZNJ598coHr5V1eUpGIffD/kdiRTJGIHTtt2/lPO87hp52Dp8Ox0+Fw/tPR6OGno4pGowVOS1I06sROO07+07Z9+Gn74OmIbNt7R14dbZm8prjHyUtsO1Lsc89LIhGniPXJe89ZKW99OrQtOHTaW/ftofW/8G2E1xS13fPqX4aK2pZ7Td7zsqj9k5dEIk6+dSv/Ptd792/R+1zvtR7tdYTXHL7fKmyf6zVFv97z1rbr0H628NewXpP3GqDgPtd7vaV7XV7Y64gjt3uRg7cf1QknNJNlBT3+XsOWZQV1wgnN5DjRoy6TF94/RSKOmjU7WVKgXB4nt5cpEnF04oknKRCwyvw4ub1MlhVUkyZNJQXK/Di5vUzRqNSs2clynKjn1ie3H6fClikexg0ibd26VZIKPWKpQYMG2rFjh3JyciRJGRkZqlevXoEPycr72bzbKomlS7+XJP3ww2L98MNiSdLixQu0atVySdKCBd9q7drVkqR5877W+vXrJEmzZ3+uzZs3SJJmzZqhjIx0SdLMmZ9o+/bcDwSfNm2Cdu/eLUmaPHmsDhzYL9u2NXnyWNm2rQMH9mvy5LGSpN27d2vatAmSpO3bszRz5icHlzlds2bNkCRt3rxBs2d/Lklav36dli3z3pFX2dnZhS7Trl27ElhVuLymwh4nr70AW7ZsUZHPvbznm5esXbuq0PVpxYqlicwq0vr16zRv3teSpLVrV2vBgm8Pnr82kVkFfPPNLElFbyO8pqjt3ldfzUx0WqGK2pZ7Td7zsqj9k5ds3Lg+tj6tWrVcixcvkJS7z127dlUi0wpV1D53y5ZNicwqVCQSKfJ1hNfMmze7yNdG2dnZiUwrVFGv9/Key16xalXuH0+Leg3rNd9//z9JBfe5XnxtsHfvvhK/Li/sdcSR2728x+n77+drxoxJsm3b0+815s37WrZt69NPJ2v+/LlHXSavvH+aNWuGtmzZVC6PU0Us06xZ07Vnz+4yP05uL5Nt2/rkk3HKzMwol8fJzWX66acfNWvWDH377VeeW5/cfpyOXKa8ruIEol78k46kpUuX6oorrtBTTz2lyy67LHb+/PnzNWjQIL300kvq3bt3vp8ZOnSo3njjDf3vf/9TzZo1df311ysjI0NTpkzJdz3HcdS6dWsNHjxYDz/8cIm60tO3y7JCsUGDYDB4cDQwoGAw968DgcCh05YViP3VwLIsWZYl2w7LsoKyrNxR5GDw0OlQKBgbXQ6Fcj/33LbtfKeTkpIUjTqy7YiSkpIOjqQfOu04EYVCeacdhUIhOU5ElmWpQYOauviVr/XDpsQO0pzWuKY+GXKesrL26MCBnALLZFlSvXo1PNW6bdtuOY4KfZxCIUt161ZPeG9ea0bGrthfAY587iUnJ3nuvs3M3C3bjhRYn6So556z27fvVU5OWI4TVSiUuy2IRnNPBwJS/fqJv28PPQ92SrIK3UZUqZKiOnWO8Uzr9u17tX9/dqHbvUAgqgYNaiW8Vcrfe+BAdoFteXJyyBP36+GtmZm7FY2q0P1TcnKSJ3oPbw2H7di6JUUVDOaeDoWCnlq/crcFdqH7XC/uw7Ky9si2nQKvI6pW9ca24PDWjIydchwV+tqoSpVkT+1vc7cFOYW+3pMcT2y7Dq1fuxSNBgp9DZuS4o1tweG9GRm7JAXkOJF8+9xg0IuvZ3frwIFwiV6XF/Y64sjtnpT7OIXDOdqyZZOaNGkqx3E8+17DcaKyLEsbN65Xw4aNlZycXOQyeeH9UzgcVmZmuho2bCwpWubHye1lysnJUUbGFjVqdLwcxynT4+T2MknShg3r1LhxU4VCoQp57pV2maLRqLZu3awGDY5TKBT01PpU0c892w6rUaO6xW77PPXtbPFISUmRpNjRRofL++tUlSpVYv+P53olEQyGFI3q4BvcQ+flyXvCHP10Uux0UlLpTgcClpKScg8kO/Ri5WingwqFvHfgWSAQKHKZvMayrNgGO8+hdm/NoQ2FgrLtwMHT+Z97Xrxvg0FLecPZ+denYBE/kVi5G93c0/m3Bd66b0OhkGzbKXK74DVFbfe8+jwoalvuNcGgJdt2itwneUnutiB08PTh61bQc+uXdPT9r9cEAoEi97lek7ftkgpuF7y2v5Xie+3nBblvKJwiX8N6Te5rGafAPtebr2et2HM13tflhb+OKPx0UlKyTjihWex28njtvUZeWl7r0Zcv8e+fUlJSDk65OqQsj5Pby5SSkqLjjz8x3+8ty+Pk9jI1bXpSiZavPJ57pV2mI58HXlqfEvXcOxrvbYWLceyxx0rKnap2pIyMDNWuXVvJycmScqetZWZmFpg/n/ezebcFAAAAAF5k22HNnPmJvPqZhoej1T0m9dLqb8YNIjVs2FB169bVsmXLCly2ZMkStWrVKvbv1q1ba//+/VqzZk2+6y1evDh2OQAAAAB4lWUF1bZtx9g0IS+j1T0m9dLqb8YNIklSz549NWvWLG3evDl23ty5c7Vu3TqlpaXFzrvwwguVlJSk0aNHx86LRqP64IMP1LBhQ3Xo0KFCuwEAAACgJCzLUsOGjTw5VfZItLrHpF5a/c1zE6Hfe+897dq1K/bNaV988YW2bNkiSbruuutUo0YN3XrrrZo2bZoGDx6swYMHa9++fRo5cqRatGihyy+/PHZbxx13nAYPHqyRI0fKtm21bdtWM2fO1Pz58/X888/nm0cIAAAAAF4TDudOt0lNvdjTn8En0eomk3pp9TfPDSK99dZb2rhxY+zfM2bM0IwZuV97d8kll6hGjRpq1KiR3nvvPT399NN64YUXlJSUpG7duumBBx6IfR5SnnvvvVe1atXSmDFjNG7cODVr1kzPPfec+vbtW6HLBQAAAAAlFQwG1blzVyP+AE6re0zqpdXfPDeI9Pnnn8d1vebNm2vkyJHFXs+yLN1yyy265ZZbypoGAAAAABXKsizVq1c/0RlxodU9JvXS6m9M/AMAAAAAjwqHw5o06UOFw97/9iha3WNSL63+xiASAAAAAHhUKBRUt249FQp5f7oNre4xqZdWf/PcdDYAAAAAQK5AwFLNmrUSnREXWt1jUi+t/saRSAAAAADgUeFwWOPH/8eI6Ta0usekXlr9jUEkAAAAAPCoUCiktLR+CoW8P4mEVveY1EurvzGIBAAAAAAeFgolJTohbrS6x6ReWv2LQSQAAAAA8CjbtjV58ljZtp3olGLR6h6Temn1NwaRAAAAAMCjQqGQ+vS5wojpNrS6x6ReWv2NQSQAAAAA8DDbNudDf2l1j0m9tPoXg0gAAAAA4FG2bWvatIlGTLeh1T0m9dLqbxyzBQAAAAAelZSUpP79ByY6Iy60usekXlr9jSORAAAAAMCjolFHu3btVDTqJDqlWLS6x6ReWv2NQSQAAAAA8CjbjujLL2fItiOJTikWre4xqZdWf2M6GwAAAAB4VFJSkvr2vTLRGXGh1T0m9dLqbxyJBAAAAAAe5TiOtm3LlON4f7oNre4xqZdWf2MQCQAAAAA8KhKJ6LvvZisS8f50G1rdY1Ivrf7GdDYAAAAA8KikpCT16nVpojPiQqt7TOql1d8YRAIAAACABPvxx5V6661/acmSxcrJyVbjxk10ySWX6fLLr1JGRrp+/vlnffHFTC1fvky//LJOxx7bUGPHTkp0dj6O4ygjI10NGjSUZXl70otJrZJZvbT6G/cSAAAAACTQd999q1tvvVHbt2/XDTfcpDvvvFfnnHOeMjLS5TgRLV26UJ9+Ok2ffjpd1atXV/36DRKdXKi8Vsfx/tQgk1ols3pp9TeORAIAAACABNm7d4+eeOJvOvvsrnriiWcKPRoiNfVinX56Zz344KMKhUL6y1/u0tq1axJQe3ShUJJSUy9OdEZcTGqVzOql1d84EgkAAAAAEuTTT6cpK2ubbr75dlmWpf379+f7pijHcbRx43rVrVtPoZC3jwHIazXhm65MapXM6qXV3xhEAgAAAIAEmT//Ox1zzDHKyNiqgQMv029/e54uuqibnn/+KWVnZ8txHK1evdKIN7m0usekXlr9zdtD2QAAAADgY7/++qsikYgefPAe9enTT7fe+kctWrRAY8eO0e7du/X440+qe/eeic6MSygUotUlJvXS6m8ciQQAAAAACbJ//z4dOHBAaWkX66677lO3bj101133qV+/y/TZZzP0yy8/a926NUZ88K/jRGh1iUm9tPobg0gAAAAAkCApKSmSpNTUi/Kd/9vfpkmSli1bevAzW6IV3lZSjhOl1SUm9dLqbwwiAQAAAECC1K/fQJJUt27dfOfXqZP777179+rccy/w/IdqS7lTg2h1h0m9tPobg0gAAAAAkCAtW7aWJGVkZOQ7PzMz9981a9bS6tUrFYl4f7pNJBKh1SUm9dLqbwwiAQAAAECC9OiRKkmaPHlivvMnTZqgYDCo00/voKysTEWj3p9uE41GaXWJSb20+hvHbAEAAACASywrIMsKFHn5qaeeqr59+2nSpIlynIg6dDhDixYt0Geffarrr79Rxx/fRMcf30SrV6/S119/JUnauHGD9u7do3feeUuS1Lx5c513XrejdjhO1PXPfQmFQurSpaurv6O8mNQqmdVLq78xiAQAAAAALrCsgGrVrqZQ8OgTQJ566h9q1qypxo0bp6++mqXGjRvrwQcf1A033BC7zoYNP2v48Dfy/Vzev/v3769LLul91N9hRxzt3LHP1YGkSCSiVauWq0WLUxUMBl37PeXBpFbJrF5a/Y1BJAAAAABwgWUFFApauvODRfpp655irt1eVS9trxMP/uvDXdKHr3x92OUNdModIwr9yaWSLs533fxOOba6hl7dQZYVcPlopKj2798nyYSpQSa1Smb10upnDCIBAAAAgIt+2rpHP2zalegM1wWDIXXs2CXRGXExqVUyq5dWf+ODtQEAAAAAZRaJRLRkyUIjvunKpFbJrF5a/Y1BJAAAAAAAABSL6WwAAAAAgDILBoNq165jojPiYlKrZFYvrf7GkUgAAAAAgDKLRGwtXDhPkYid6JRimdQqmdVLq78xiAQAAAAAKAcBVa1aTVIg0SFxMKlVMquXVj9jOhsAAAAAoMyCwaBat26b6Iy4mNQqmdVLq79xJBIAAAAAoMxs29a8ebNl296fGmRSq2RWL63+xiASAAAAAKDMAoGA6tatr0DA+1ODTGqVzOql1d+YzgYAAAAAKLNgMKjmzVslOiMuJrVKZvXS6m8ciQQAAAAAKDPbtjVnzhdGTA0yqVUyq5dWf2MQCQAAAABQZpYVUJMmTWVZ3p8aZFKrZFYvrf7GdDYAAAAAQJlZVlDNmp2c6Iy4mNQqmdVLq79xJBIAAAAAoMxs29asWTOMmBpkUqtkVi+t/sYgEgAAAACgzCzLUvPmrWRZ3n+baVKrZFYvrf7GdDYAAAAAQJlZlqUmTZomOiMuJrVKZvXS6m8MtwEAAAAAysy2w5o58xPZdjjRKcUyqVUyq5dWf2MQCQAAAABQZpYVVNu2HWVZwUSnFMukVsmsXlr9jelsAAAAAIAysyxLDRs2SnRGXExqlczqpdXfOBIJAAAAAFBm4XBYU6dOUDjs/alBJrVKZvXS6m8MIgEAAAAAyiwYDKpz564KBr0/NcikVsmsXlr9jelsAAAAAIAysyxL9erVT3RGXExqlczqpdXfOBIJAAAAAFBm4XBYkyZ9aMTUIJNaJbN6afU3BpEAAAAAAGUWCgXVrVtPhULenxpkUqtkVi+t/sZ0NgAAAABAmQUClmrWrJXojLiY1CqZ1Uurv3EkEgAAAACgzMLhsMaP/48RU4NMapXM6qXV3xhEAgAAAACUWSgUUlpaP4VC3p/wYlKrZFYvrf7GIBIAAAAAoFyEQkmJToibSa2SWb20+heDSAAAAACAMrNtW5Mnj5Vt24lOKZZJrZJZvbT6G8dsAQAAAADism/fPv3nP+/qhx+WacWKH7R79y499NDf1Lt3X4VCIfXpc4VCoZC6du1U5G106tRZL7/8RgVWF3R4qwlM6qXV37inAAAAAABx2blzh0aNelMNGx6nU05prkWLFuS73LbDCoVCeuSRvxf42ZUrV+jDD/+jzp3Pqqjco8prNYVJvbT6F9PZAAAAAABxqVevviZOnKaPPpqsO+64M99ltm1r2rSJsm1bF13Uu8B/+/fvUyAQUGrqRQmqL7zVBCb10upvDLcBAAAAAOKSnJysevXqF3pZUlKS+vcfWOhlOTk5mjXrc51+ekcde2xDNxPjcrRWLzKpl1Z/40gkAAAAAECZRaOOdu3aqWjUKXDZ3LlztGfPbvXs2SsBZQUdrdWLTOql1d8YRAIAAAAAlJltR/TllzNk25ECl3366VQlJyere/cLE1BW0NFavcikXlr9jelsAAAAAIAyS0pKUt++VxY4f+/ePfrmmzk666xzVaNGjQSUFVRUq1eZ1Eurv3EkEgAAAACgzBzH0bZtmXKc/FODZs36XDk52erZMy1BZQUV1epVJvXS6m8MIgEAAAAAyiwSiei772YrEsk/NWjGjGmqXr26zjnnvASVFVRUq1eZ1EurvzGdDQAAAABQZklJSerV69J852VmZmrRovnq1auPkpOTExNWiMJavcykXlr9jSORAAAAAABl5jiO0tM355sa9Nln0+U4jme+lS1PYa1eZlIvrf7GkUgAAAAAAFlWQJYVKPZ6H374gXbv3qPMzAxJ0jfffK3MzAw5TkSnnHKSGjZsqFAo93iFTz+dpgYNGujMM8+UZcV/DIPjROU40dItSFy3H9HSpQvVvXvPEnUlikm9tPobg0gAAAAAUMlZVkC1aldTKFj8G+kPPnhfGzdujP171qzPNWvW55Kkzz77TA0a1JIkrV27VitXrtCNN96oevVK9q1sdsTRzh37XBtICoWSlJp6sSu37QaTemn1NwaRAAAAAKCSs6yAQkFLd36wSD9t3XPU61a99HGdUsRlt4z7WdLPsX+fcscIfS3p61e+jrvllGOra+jVHWRZAdcGkRzH0ebNG9So0fFGHIFiUi+t/sYgEgAAAABAkvTT1j36YdOuRGe4znEcrV69Ug0bNjZi8MCkXlr9jUEkAAAAAEClEgqF1L17z0RnxM2kXlr9jUEkAAAAAEClkJOToxEjhmn69CnatWunTjmlhW6++TadeeZZiU4rlEm9tFYOHK8FAAAAAKgU/vGPxzRmzPtKTb1IffteIsuydO+9d2rx4u8TnVYok3pprRw4EgkAAAAA4HvLly/TZ5/N0O2336lrrrlOkpSdna3Bgwfon/98RcOGvZXgwvxM6qW18uBIJAAAAACA782a9ZmCwaD69euvSCSi1atXKhQKqU+fflq2bInS07ckOjEfk3pprTwYRAIAAAAA+N6qVT/qhBOa6phjqisajSorK1PRaFStW58mSVq9elWCC/MzqZfWyoPpbAAAAAAA39u2LVP16tWXlPutXF26dJWk2HmZmRkJayuMSb20Vh4ciQQAAAAA8L3s7GwlJSVJkiKRiFasWKpIJKLk5GRJUk5OdiLzCjCpl9bKo1IMIuXk5Oi5555T165d1a5dO1155ZWaM2dOorMAAAAAABUkJSVF4XD44L+i2r9/n6SocnJyJEnJySkJayuMSb20Vh6VYhDpgQce0L///W/17dtXDz/8sILBoG6++WbNnz8/0WkAAAAAgApQr159bduWKUkKBkPq2LGLgsFQ7Lz69RskMq8Ak3pprTx8P4i0ZMkSffLJJ/rzn/+s+++/XwMGDNDbb7+txo0b6/nnn090HgAAAACgAjRv3lK//rpee/fuUSQS0ZIlCxWJRLR8+bKDl7dIcGF+JvXSWnn4fhBp2rRpCgaDGjBgQOy8lJQUXXHFFVq0aJE2b96cwDoAAAAAQEXo3v1CRSIRTZw4PnZeTk6OpkyZpFNPbaOGDY9LYF1BJvXSWnn4/tvZVqxYoWbNmql69er5zm/Xrl3s8kaNGiUiDQAAAABQQU47rY0uuCBV//rXa9qxI0tNmpygf/7zNW3evEkPPPBIovMKMKmX1srD94NIGRkZatCg4JzGvPO2bt1aottzHFuWFVIkEpEkBYNBRSK2pICCwaBs21YgcOi0ZQVkWXmnLVmWJdsOy7KCsixL4XBYweCh06FQUIFA3unch8e27Xynk5KSFI06su2IkpKS5DiOIpFDpx0nolAo77SjUCgkx4koEolKkk5rXFNVk4OlvUvLxUn1j5EkRaNR2Xa4wDJFo44kr7U6CgRU6OMUjXrjvs1rjUQiikadQp97wWCSJ1qlQ715z9sj16e89cxLrZIUjUbkOFGFQqGD93X04HrmjeftoeeBrUDAKmIbkeKpVkmy7cK3e7nPicS3Svl7I5GC2/K8A3y91Oo4uduuwvZPXuk9vDUSsWPrlhRVMJh7+uDq5ZlWKXe/UNg+15v7sKikaIHXEV7ZFkj5t13RqAp9bRQM5n5zTqJ7828LCn+955Vt16H1K6JAIFDoa1ivbAuk/K9lAoGAHCf/Ptebr2ed2OvZI/e5kYg3tgeFv57Nv8/1yutZqWBvYe+fQqGgHCciy7IUCFgH18Xc7tzzD50OBkMHt9mOHnvs//Svf72hadOmaPfuXTrllOZ67rkX1alTp4PXiSoYDOY77TiOotFDp6WoLCsYe+0XCAQOflBz4e8JQyErtj+zrMBhvVaxp//2t79r+PB/HqXXybd8hZ8+tEy2nXvfHP4a9sh9rmUFYu8hpcDBbVruOpl3+tByHDr9yCN/04gRjTR9+hTt2pXb+sILQ9Whw+myLJXoccprCYfto77PlZzY/V7c42RZVmyZHnvs/zRs2Ov5Wp9//mV16tSpVI+TZQUVjUaVkxMul/fuhb3XOPJxksp3PMK28z5s/OgC0bythU+lpqbqN7/5jd5888185//6669KTU3Vgw8+qBtuuCExcQAAAAAAAIbw/WciValSJfZVfYfLzs6OXQ4AAAAAAICj8/0gUoMGDZSRkVHg/Lzzjj322IpOAgAAAAAAMI7vB5FatWqldevWac+ePfnOX7x4sSSpdevWicgCAAAAAAAwiu8HkdLS0hSJRDRmzJjYeTk5ORo3bpzat2/PN7MBAAAAAADEwfffzta+fXulpaXpxRdf1LZt23TiiSdq/Pjx2rhxo/7xj38kOg8AAAAAAMAIvv92Nin3Q7RffvllTZo0STt37lTLli1155136rzzzkt0GgAAAAAAgBEqxSASAAAAAAAAysb3n4kEAAAAAACAsmMQCQAAAAAAAMViEAkAAAAAAADFYhAJkqScnBw999xz6tq1q9q1a6crr7xSc+bMKfbnPv30U910003q2rWr2rRpo/PPP19DhgzRqlWrjOjavn27RowYoUGDBumss85Sp06ddNVVV2nKlCme7D3S+vXr1bZtW7Vs2VJLly71ZOuePXv07LPPqkePHmrTpo3OO+88DRkyRPv37/dUa3Z2tv71r3+pd+/eat++faxz9erVcXe6sQxHuvHGG9WyZUv9/e9/N6Jr8+bNeu2113TFFVfozDPPVJcuXXTdddfpm2++8VzrkebPn6+WLVuqZcuWysrK8mRrZmamHn30UZ133nlq27atevTooYceeqjEv6cienfv3q1nn31WPXv2VLt27XTBBRfooYce0qZNm0r8e8qr/9VXX409xof/17Zt2zI3VUSXF9av0t6HZVm/KrK3PNaximj14vqVZ8qUKRowYIBOP/10derUSVdffbXmzp3r+a7yXL8qovdIidyHxduayPWrJK1url9lWYYePXoUuo1o2bKlevbs6fkuL+zDSnsflnUfZrpQogPgDQ888ICmT5+uwYMHq1mzZho/frxuvvlmvf322+rUqVORP/fjjz+qZs2aGjx4sOrUqaPMzEx99NFHuvLKKzVmzBi1atXK013ff/+9Xn75ZZ1//vm67bbbFAqFNH36dN1999366aefNGTIEE/1HunJJ59UKBRSTk5OiTorqnX37t269tprtWXLFg0YMEBNmzZVVlaWFixYoJycHFWtWtUzrffee68+//xzXXnllTrttNOUnp6u0aNHa8CAAZo0aZKaNGkSV2t5L8PhZsyYoe+//75MHRXd9dlnn+nNN99Uamqq+vfvL9u2NXHiRN1444168skndfnll3um9XCO4+iJJ55QtWrVtG/fvrgbK7J18+bNGjhwoCTp6quvVsOGDbV161YtWbLEc72O4+jGG2/UmjVrNHDgQP3mN7/RL7/8otGjR2v27NmaMmWKqlevXuLu8up/7LHHVK1atdi/g8FgqVsqsstL61dJ7sOyrl8V1Vte65jbrV5ev1599VW9/vrruuiii2LP0VWrVik9Pb3UPRXVVZ7rV0X0Hi6R+7B4W72wfsXT6vb6VZZleOihh7R37958523atEkvv/yyzj333DI1VUSXF/ZhpbkPy2MfZrwoKr3FixdHW7RoER0xYkTsvAMHDkRTU1OjAwYMKPHtZWRkRE899dToI4884vmu9evXRzds2JDveo7jRAcPHhxt06ZNdO/evZ7qPdxXX30VPe2006IvvfRStEWLFtElS5Z4rvVvf/tbtFOnTtH169eX+DYrsnXLli3RFi1aRJ9++ul81507d260RYsW0VGjRpW6Pxotn2U4cOBA9IILLoi+9tpr0RYtWkQff/zxMjVVVNeqVaui27Zty3dednZ2NC0tLXr++ed7qvVwo0ePjnbu3Dn6xBNPRFu0aFFgGbzQ+vvf/z7ao0ePaFZWVtxtiepdsGBBtEWLFtH33nsv3/ljx46NtmjRIjpjxoyE9L/yyislfny91OWF9as092FZ1q+K7C2PdawiWr26fi1atCjasmXLMu9DE9VVXutXRfUeLlH7sJK0Jnr9irfVzfUrGi3/17mvv/56tEWLFtEFCxZ4vssL+7B4Ww9X1n2YHzCdDZo2bZqCwaAGDBgQOy8lJUVXXHGFFi1apM2bN5fo9urVq6cqVapo9+7dnu864YQTChxhEggElJqaqpycHP3666+e6s0TDof1j3/8Q4MHD1bTpk1LdLsV1bpr1y6NGzdOV111lU444QTl5OSU6oipimjds2ePJKl+/fr5rtugQYPY7yuL8liGN998U9FoVDfddFOZWiq6q3nz5qpbt26+85KTk9WtWzdt2bIldt97oTXPjh079PLLL2vIkCGqWbNmXH0V3bpmzRp99dVXuummm1SnTh1lZ2crHA6XuLWievMe53r16uU7vzzWsfLaRuzZs0fRaLTUHYno8tL6VVxrnrKuXxXVW17rWEW0enX9evvtt1W/fn0NHjxY0Wi0wF/7y6Iiuspr/aqo3jyJ3IfF2+qF9SveVjfXr7IuQ2EmT56s448/Xh07dvR8l9f2YUdrzVMe+zA/YBAJWrFihZo1a1bgUMx27drFLi/Orl27lJWVpR9//FEPP/yw9uzZo7PPPtvYrszMTElSnTp1PNn79ttva9euXbr99tvj7qvo1gULFig7O1snnniihgwZotNPP13t2rXT1VdfHdftV2Rr06ZNddxxx2nUqFH6/PPPtWXLFi1ZskSPPfaYjj/+eF188cVx97qxDJs2bdKbb76pe++9V1WqVClTi1e6MjIyVLVq1binNFZk69ChQ9WgQQNdffXVcbUlojXv8xrq16+v66+/Xu3atVP79u31+9//Xhs2bPBcb5s2bVStWjUNHTpUc+fOVXp6ur777js999xzatu2rc4555wSNZdnvyRdeOGFOuOMM9SxY0fde++9sX1AWSSyq6LXr5K0lnX9qqje8lrHKqLVq+vX3Llz1bZtW73zzjs666yz1LFjR3Xt2lXvvfdeqXu80FXS9auiexO5D4u31QvrV7ytbq5fZV2GIy1fvlxr1qxRnz59ytSU6K5E7MPibS2PfZgf8JlIUEZGRmw0/XB5523durXY27jqqqv0888/S5KqVaum2267TVdccYWRXTt27NCHH36oTp066dhjj/Vcb0ZGht544w3df//9pZ6DXRGtv/zyiyTphRdeUNOmTfXMM89o9+7dev3113X99ddr8uTJcd2/FdGalJSkV199Vffcc49uu+222PmnnXaaPvjggzL/paGsy/D000+rdevWZR7M8krXL7/8ok8//VRpaWlxf/ZMRbWuXLlSY8aM0fDhw0v9uTgV0bpu3TpJ0iOPPKK2bdvqpZdein1A5Y033qiPP/447hdfFdFbt25dvfTSS/rrX/+qG264IXZ+165d9corrygUKv3LkbL016xZU9dee61OP/10JScna/78+Ro9erSWLl2qjz76qEyfc5Goropev0rSWh7rV0X1ltc6VhGtXly/du7cqe3bt2vhwoX69ttv9cc//lGNGjXSuHHj9H//938KhUJlehOWqK7SrF8V2ZvIfVhJWhO9fpWk1c31qyzLUJhJkyZJki655JIyNSWyKxGvEeNtLa99mB8wiAQdOHBAycnJBc7POzzzwIEDxd7GU089pT179ujXX3/VuHHjlJ2drUgkIssq/cFuiehyHEf33nuvdu3apUceecSTvc8//7xOOOEEXXnllSXqq+jWvMOCA4GA/v3vf+uYY46RJJ166qkaMGCA3n//fd19992eaJVyX6y3bt1aaWlpat++vdavX69//etfuvPOOzVq1KgyHa5clmX49ttvNWPGDP33v/8t9e/3Utf+/ft15513qkqVKrrnnns81/qPf/xD559/vrp27Rp3WyJa8z7IsUGDBho+fHjsuXzcccfpz3/+syZPnhz3NqKi7tu6devq1FNPVceOHXXKKado5cqVGjFihB588EG98sorcbWWd//111+f798XXXSR2rVrp3vvvVejR4/WzTffbFRXItavkrSWx/pVUb3ltY5V1H3rtfUr7/7bsWOHXnrpJfXu3VuSlJaWpr59++qf//xnmQaREtFV2vWrInsTuQ8rSWui16+S3q9urV9lWYYjOY6jTz75RKeeeqpOPvnkMjUlqisR+7CStJbXPswPmM4GValSpdDPqsnOzo5dXpwOHTrovPPO0zXXXKORI0fq448/1osvvmhc1//93//p66+/1hNPPFHib5ariN7vv/9eEydO1IMPPlimAbqKaM27jQsuuCA2gCRJp59+uo4//ngtWrTIM627d+/WoEGDdPrpp+uee+5Ramqqfve73+nVV1/VggUL9NFHH8XVWt7LYNu2/vGPf6hfv36xQ3LLU0V3RSKR2DcfDh06VA0bNvRU65QpU7Ro0SLdf//9cXclqjXvNtLS0vJtC9LS0hQKhbRw4UJP9f76668aPHiwLr/8ct16661KTU3VH//4R/3tb3/T9OnT9eWXX8bdW179Renbt68aNGhQ6q/xTlRXItavkrSW1/qV1+N2b3mtYxXR6sX1K+/NW1JSki666KLY+ZZlqVevXtqyZUuZvh69orvKsn5VVG+i92ElaU30+lWSVjfXr7Isw5G+++47paenq2/fvmXqSVSXF/ZhR2stz32YHzCIBDVo0EAZGRkFzs87ryRTuiSpVq1aOuuss2KHA5rS9dprr2n06NG65557dOmll3qy97nnnlOnTp10/PHHa8OGDdqwYYO2b98e+z3xviCriNa82zjyw6ql3A8n3LVrl2dap0+frszMTPXo0SPfdTt37qzq1auX6A15YUq7DBMmTNDPP/+sAQMGxB7vvM8K2Lt3rzZs2KD9+/cb0/XXv/5Vs2bN0tNPP13iz0yriNZnn31WF110kZKSkmLXy3uebtmyJe6vpK6I1qLWr2AwqNq1a8e9flVUb94RgBdccEG+28hb58qyjpX3NkLK/Wv4zp07S92UiK5ErF8laS2v9auiestrHauIVi+uX7Vr11ZKSopq165dYNpH3gcUl2Q7leiusqxfFdWb6H1YSVoTvX6VpNXN9assy3CkSZMmybKscvvog4ru8sI+7Git5bkP8wOms0GtWrXSvHnztGfPnnyfXbB48WJJUuvWrUt8mwcOHCjzt7NVZNf777+vV199Vddff32ppy9URO/mzZu1ceNGXXjhhQWue9ttt6lGjRqaP3++J1pPO+00SSp0o7p161addNJJcd1uRbRu27ZNUu5fQQ4XjUblOE6B80uqtMuwefNmhcNhDRw4sMBlEyZM0IQJE/T6668rNTXV813PPPOMxo0bp4ceeqhUH/hYEa2bN2/W5MmTNXny5ALX7d+/v1q1aqWJEyd6orWo9SsnJ0fbt28v8G0nie7dtm2botFogXXJtm1JBde9kijvbUQ0GtXGjRt16qmnlrqporsStX6VpLW81q+K6i2vdawiWr24flmWpdatW2vp0qXKycnJN9Uk7/NJSvLlJYnsKuv6VVG9id6HlaQ10etXSVrdXL/KsgyHy8nJ0YwZM9S5c+cSHyXnhS4v7MOKay3PfZgfcCQSlJaWpkgkojFjxsTOy8nJ0bhx49S+fXs1atRIUu438KxZsybfz+a9+T7chg0bNHfuXLVp08aIrilTpuiJJ55Q37599eCDD3q69+9//7tef/31fP9dd911kqT7779fzz//vGdaTzrpJLVq1UqfffaZsrKyYufPnj1bmzdvjvvbLCqitVmzZpJynwuH++yzz7Rv375SDVSVxzL07t27wOP9+uuvS5K6deum119/vUzT3Cqqa8SIEXrrrbd06623FviMDy+1Fna9vM9JeOaZZ+LePlREa5cuXVSvXj1NmjQpdri2JI0fP16RSKRE3xZTEb3NmjVTNBrV1KlT8/3uvBdjZRmwKcs24vBtU57Ro0crKytL5513XqmbKrIrketXSVrLa/2qqN7yWscqotWr61evXr0UiUQ0YcKE2HnZ2dmaNGmSTjnllDK92a2orvJYvyqqN9H7sJK0emH9irfVzfWrrMuQ58svv9SuXbvKbSpbRXYleh8Wb2t57sP8gCORoPbt2ystLU0vvviitm3bphNPPFHjx4/Xxo0b9Y9//CN2vfvvv1/fffedfvzxx9h5ffv21dlnn61WrVqpVq1aWrdunT766CPZtl3iDx1MRNeSJUv0l7/8RbVr19bZZ5+tjz/+OF9Dx44ddcIJJ3imt7APcss7lPLMM89U27ZtPdMqSQ8++KB+97vf6ZprrtHVV1+t3bt3a9SoUWrWrFmhRy8kqvWCCy5Q8+bN9frrr2vTpk1q3769fvnlF73//vtq0KBBmb9psLTLcPLJJxf5IYTHH398qY9AqsiuTz/9VM8995yaNWumk046qcBfac4999xCpzwmorWw+zPva2HPP//8uP8yWhGtycnJ+stf/qL7779fgwYNUr9+/bRp0ya9++676tSpk3r27BlXa0X19u/fX2+99ZYeffRRLV++XM2bN9cPP/ygsWPHqnnz5mV6LpdlG3HBBReod+/eatGihZKTk7Vw4UJ98sknat26tQYMGFDqporqSvT6VZLW8lq/Kqq3vNaximj16vp19dVXa+zYsfr73/+un3/+WY0bN9bEiRO1adMm/fOf/yx1U0V1ldf6VVG9id6HlaTVC+tXvK1url9lXYY8kyZNUnJycr7Pdyqriujywj4s3tby3If5AYNIkJQ7z/Pll1/Wxx9/rJ07d6ply5YaNmyYzjzzzKP+3MCBAzVr1ix9/fXX2rt3r+rWratzzz1Xt9xyi1q2bOn5rp9++knhcFhZWVl66KGHCtzOU089FfcgUkX0lqeKaD3rrLM0YsQIDR06VC+++KKqVq2q1NRU3Xffffk+bDvRrcnJyXr//ff1xhtvaNasWZo8ebKOOeYYpaam6u677y6XHUNpl8FtbnetXLlSUu7X+f7lL38pcPk777wT94twr96HhamI1ksvvVRJSUkaPny4nn32WdWsWVMDBgzQ3XffXeKvnnW7t06dOvroo480dOhQffHFF/rggw9Uu3ZtXX755br77rsL/VaViujv27evFi1apOnTpysnJ0eNGzfW73//e916661xfb10oru8sH65fR8msre81jG3W726flWpUkVvv/22nnvuOY0bNy52ZO+//vWvMh/pVxFd5bl+VURveaqI1kSvX/G2ur1+lWUZJGnPnj2aNWuWunfvrho1apS5pSK7vLAPi7cV+QWi0Wg00REAAAAAAADwNj4TCQAAAAAAAMViEAkAAAAAAADFYhAJAAAAAAAAxWIQCQAAAAAAAMViEAkAAAAAAADFYhAJAAAAAAAAxWIQCQAAAAAAAMViEAkAAAAAAADFYhAJAAAAAAAAxWIQCQAAoAxeffVVtWzZMtEZAAAArgslOgAAAMBLxo0bpwcffDD27+TkZNWqVUstW7ZUt27ddNlll6l69epl+h3p6en673//q9TUVLVu3bqsyQAAABWCQSQAAIBCDBkyRMcff7xs21ZmZqa+++47Pfnkk/r3v/+tN954Q61atZIk3Xbbbbr55ptLdNtbt27Va6+9piZNmjCIBAAAjMEgEgAAQCHOP/98tW3bNvbvW265RXPnztWtt96q22+/XVOmTFGVKlUUCoUUCvGSCgAA+B+fiQQAABCns88+W7fffrs2btyojz/+WFLhn4k0Z84cDRw4UJ06dVKHDh100UUX6cUXX5QkzZs3T1dccYUk6cEHH1TLAfsfRQAABmhJREFUli3VsmVLjRs3TpI0f/58DRkyRN27d1ebNm3UrVs3Pfnkkzpw4EC+3/HAAw+oQ4cOSk9P1+23364OHTrorLPO0jPPPKNIJJLvuo7j6O2331bfvn3Vtm1bnXXWWbrpppu0dOnSfNebOHGiLrvsMrVr106dO3fW3Xffrc2bN5ffHQgAAIzGn80AAABKoF+/fnrxxRc1e/ZsXXXVVQUuX716tW655Ra1bNlSQ4YMUXJysn755RctXLhQknTyySdryJAheuWVVzRgwACdccYZkqSOHTtKkqZNm6YDBw5o4MCBql27tpYsWaL33ntPW7Zs0SuvvJLvd0UiEd10001q166d/vKXv2ju3Ll66623dMIJJ+iaa66JXe/hhx/WuHHjdP755+uKK65QJBLR/PnztXjx4tjRVv/85z81dOhQ9erVS1dccYWysrL03nvvadCgQZowYYJq1qzpyv0JAADMwSASAABACRx33HGqUaOGfv3110IvnzNnjsLhsN58803VrVu3wOX169fX+eefr1deeUWnn366+vXrl+/ye++9V1WqVIn9e8CAATrxxBP14osvatOmTWrcuHHssuzsbPXq1Ut33HGHJGngwIHq37+/xo4dGxtE+vbbbzVu3Dhdd911+utf/xr72d/97neKRqOSpI0bN+rVV1/VXXfdpVtvvTV2nZ49e6p///4aPXp0vvMBAEDlxHQ2AACAEqpWrZr27t1b6GV5R+x89tlnchynxLd9+ADSvn37lJWVpQ4dOigajWr58uUFrj9w4MB8/z7jjDO0YcOG2L9nzJihQCCgP/7xjwV+NhAISJI+/fRTOY6jXr16KSsrK/Zf/fr1deKJJ2revHklXg4AAOA/HIkEAABQQvv27VO9evUKvax379768MMP9de//lUvvPCCzj77bP32t79VWlqaLKv4v99t2rRJr7zyij7//HPt3Lkz32V79uzJ9++UlJQCRzvVqlUr38+tX79exx57rGrXrl3k71y3bp2i0ah69uxZ6OV8cDgAAJAYRAIAACiRLVu2aPfu3WratGmhl1epUkXvv/++5s2bp1mzZunrr7/WlClTNGbMGL311lsKBoNF3nYkEtGNN96onTt36ve//71OOukkVatWTenp6XrggQcKHNl0tNsqCcdxFAgE9OabbxZ6m9WqVSuX3wMAAMzGIBIAAEAJTJw4UZLUtWvXIq9jWZbOPvtsnX322XrwwQc1bNgwvfTSS5o3b57OOeec2DSyI61atUrr1q3TM888o0svvTR2/pw5c0rd27RpU82ePVs7duwo8mikpk2bKhqN6vjjj9dvfvObUv8uAADgb3wmEgAAQJzmzp2rN954Q8cff7wuueSSQq+zY8eOAue1bt1akpSTkyNJqlq1qiRp165d+a6XN90t7wOv806/8847pW7u2bOnotGoXnvttQKX5f2enj17KhgM6rXXXsv3u/Ous3379lL/fgAA4B8ciQQAAFCIr776SmvXrlUkElFmZqbmzZunOXPmqHHjxvrnP/+plJSUQn/u9ddf1/z589WtWzc1adJE27Zt0+jRo3XcccfpjDPOkJR75E/NmjX1wQcf6JhjjlG1atXUrl07nXTSSWratKmeeeYZpaenq3r16po+fXqBwaaSOOuss9SvXz+9++67+uWXX3TeeefJcRwtWLBAXbp00bXXXqumTZvqrrvu0gsvvKCNGzcqNTVVxxxzjDZs2KCZM2fqqquu0k033VTqBgAA4A8MIgEAABTilVdekSQlJSWpdu3aatGihR566CFddtllql69epE/16NHD23cuFEfffSRtm/frjp16qhz587605/+pBo1asRu8+mnn9aLL76oxx57TLZt66mnntJll12mYcOG6YknntC//vUvpaSk6Le//a0GDRqkfv36lXpZnnrqKbVs2VJjx47Vs88+qxo1aqhNmzbq0KFD7Do333yzmjVrpn//+996/fXXJUnHHXeczj33XPXo0aPUvxsAAPhHIHrkMcsAAAAAAADAEfhMJAAAAAAAABSLQSQAAAAAAAAUi0EkAAAAAAAAFItBJAAAAAAAABSLQSQAAAAAAAAUi0EkAAAAAAAAFItBJAAAAAAAABSLQSQAAAAAAAAUi0EkAAAAAAAAFItBJAAAAAAAABSLQSQAAAAAAAAUi0EkAAAAAAAAFOv/AQn/Mn8KT3ApAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot range_ and num_outliers\n",
        "fig = plt.figure(figsize = (14, 8))\n",
        "plt.rcParams.update({'font.size': 12})\n",
        "plt.bar(list(map(str, range_)), num_outliers)\n",
        "plt.title(\"Number of outliers vs. distance of points from centroid\")\n",
        "plt.xlabel(\"Distance\")\n",
        "plt.ylabel(\"Number of outliers\")\n",
        "for i in range(len(range_)):\n",
        "  plt.text(i, num_outliers[i], num_outliers[i], ha = 'center')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNISxrzwGvBH"
      },
      "source": [
        "Depending on how sensitive you want your anomaly detector to be, you can choose which radius you would like to use. For now, 0.62 is used, but you can change this value."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PMNFFSDOTELn"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_outliers"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-a002d189-9eb2-405c-bd7a-779a247f1354\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Text</th>\n",
              "      <th>Label</th>\n",
              "      <th>Class Name</th>\n",
              "      <th>Embeddings</th>\n",
              "      <th>Outlier</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Re: Source of random bits on a Unix workstati...</td>\n",
              "      <td>11</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>[0.024040421, -0.06749866, -0.097997, -0.04392...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>* REPORT ON PRIVACY-PROTECTING OFF-LINE CASH ...</td>\n",
              "      <td>11</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>[0.0060990495, 0.01995056, -0.08278795, -0.050...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Re: Tempest\\nNntp-Posting-Host: ely.cl.cam.ac...</td>\n",
              "      <td>11</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>[-0.024053391, -0.068649784, -0.036115933, -0....</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Re: Overreacting \\nOrganization: Express Acce...</td>\n",
              "      <td>11</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>[-0.014026283, -0.04744478, -0.023989808, -0.0...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Cryptography FAQ 07/10 - Digital Signatures\\nO...</td>\n",
              "      <td>11</td>\n",
              "      <td>sci.crypt</td>\n",
              "      <td>[0.0018564886, -0.035170633, -0.066081196, -0....</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a002d189-9eb2-405c-bd7a-779a247f1354')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a002d189-9eb2-405c-bd7a-779a247f1354 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a002d189-9eb2-405c-bd7a-779a247f1354');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e03474e7-9247-40b4-acf8-2cc87669aed0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e03474e7-9247-40b4-acf8-2cc87669aed0')\"\n",
              "            title=\"Suggest charts\"\n",
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              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e03474e7-9247-40b4-acf8-2cc87669aed0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                                 Text  Label Class Name  \\\n",
              "7    Re: Source of random bits on a Unix workstati...     11  sci.crypt   \n",
              "8    * REPORT ON PRIVACY-PROTECTING OFF-LINE CASH ...     11  sci.crypt   \n",
              "11   Re: Tempest\\nNntp-Posting-Host: ely.cl.cam.ac...     11  sci.crypt   \n",
              "23   Re: Overreacting \\nOrganization: Express Acce...     11  sci.crypt   \n",
              "30  Cryptography FAQ 07/10 - Digital Signatures\\nO...     11  sci.crypt   \n",
              "\n",
              "                                           Embeddings Outlier  \n",
              "7   [0.024040421, -0.06749866, -0.097997, -0.04392...    True  \n",
              "8   [0.0060990495, 0.01995056, -0.08278795, -0.050...    True  \n",
              "11  [-0.024053391, -0.068649784, -0.036115933, -0....    True  \n",
              "23  [-0.014026283, -0.04744478, -0.023989808, -0.0...    True  \n",
              "30  [0.0018564886, -0.035170633, -0.066081196, -0....    True  "
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# View the points that are outliers\n",
        "RADIUS = 0.62\n",
        "detect_outlier(df_train, emb_c, RADIUS)\n",
        "df_outliers = df_train[df_train['Outlier'] == True]\n",
        "df_outliers.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h_wbM5yYE4MS"
      },
      "outputs": [],
      "source": [
        "# Use the index to map the outlier points back to the projected TSNE points\n",
        "outliers_projected = df_tsne.loc[df_outliers['Outlier'].index]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xCt4wfYdoTJz"
      },
      "source": [
        "Plot the outliers and denote them using a transparent red color."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IrAKwBp0TaNu"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(8,6)) # Set figsize\n",
        "plt.rcParams.update({'font.size': 10})\n",
        "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
        "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Class Name', palette='Set2');\n",
        "sns.scatterplot(data=centroids, x='TSNE1', y='TSNE2', color=\"black\", marker='X', s=100, label='Centroids')\n",
        "# Draw a red circle around the outliers\n",
        "sns.scatterplot(data=outliers_projected, x='TSNE1', y='TSNE2', color='red', marker='o', alpha=0.5, s=90, label='Outliers')\n",
        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))\n",
        "plt.title('Scatter plot of news with outliers projected with t-SNE')\n",
        "plt.xlabel('TSNE1')\n",
        "plt.ylabel('TSNE2');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RVm_A9HmGwEN"
      },
      "source": [
        "Use the index values of the datafames to print a few examples of what outliers can look like in each category. Here, the first data point from each category is printed out. Explore other points in each category to see data that are deemed as outliers, or anomalies."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lpZ-hcDvG13M"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Re: Source of random bits on a Unix workstation\n",
            "Lines: 44\n",
            "Nntp-Posting-Host: sandstorm\n",
            "\n",
            ">>For your application, what you can do is to encrypt the real-time clock\n",
            ">>value with a secret key.\n",
            "\n",
            "Well, almost.... If I only had to solve the problem for myself, and were\n",
            "willing to have to type in a second password  whenever I\n",
            "logged in, it could work. However, I'm trying to create a solution that\n",
            "anyone can use, and which, once installed, is just as effortless to start up\n",
            "as the non-solution of just using xhost to control access. I've got\n",
            "religeous problems with storing secret keys on multiuser computers.\n",
            "\n",
            ">For a good discussion of cryptographically \"good\" random number\n",
            ">generators, check out the draft-ietf-security-randomness-00.txt\n",
            ">Internet Draft, available at your local friendly internet drafts\n",
            ">repository.\n",
            "\n",
            "Thanks for the pointer! It was good reading, and I liked the idea of using\n",
            "several unrelated sources with a strong mixing function. However, unless I\n",
            "missed something, the only source they suggested \n",
            "that seems available, and unguessable by an intruder, when a Unix is\n",
            "fresh-booted, is I/O buffers related to network traffic. I believe my\n",
            "solution basically uses that strategy, without requiring me to reach into\n",
            "the kernel.\n",
            "\n",
            ">A reasonably source of randomness is the output of a cryptographic\n",
            ">hash function , when fed with a large amount of\n",
            ">more-or-less random data. For example, running MD5 on /dev/mem is a\n",
            ">slow, but random enough, source of random bits; there are bound to be\n",
            ">128 bits of entropy in the tens  of megabytes of data in\n",
            ">a modern workstation's memory, as a fair amount of them are system\n",
            ">timers, i/o buffers, etc.\n",
            "\n",
            "I heard about this solution, and it sounded good. Then I heard that folks\n",
            "were experiencing times of 30-60 seconds to run this, on\n",
            "reasonably-configured workstations. I'm not willing to add that much delay\n",
            "to someone's login process. My approach  takes\n",
            "a second or two to run. I'm considering writing the be-all and end-all of\n",
            "solutions, that launches the MD5, and simultaneously tries to suck bits off\n",
            "the net, and if the net should be sitting __SO__ idle that it can't get 10K\n",
            "after compression before MD5 finishes, use the MD5. This way I could have\n",
            "guaranteed good bits, and a deterministic upper bound on login time, and\n",
            "still have the common case of login take only a couple of extra seconds.\n",
            "\n",
            "-Bennett\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "sci_crypt_outliers = df_outliers[df_outliers['Class Name'] == 'sci.crypt']\n",
        "print(sci_crypt_outliers['Text'].iloc[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SPsQB3eHJN25"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Re: Laser vs Bubblejet?\n",
            "Reply-To: \n",
            "Distribution: world\n",
            "X-Mailer: cppnews $Revision: 1.20 $\n",
            "Organization: null\n",
            "Lines: 53\n",
            "\n",
            "Here is a different viewpoint.\n",
            "\n",
            "> FYI:  The actual horizontal dot placement resoution of an HP\n",
            "> deskjet is 1/600th inch.  The electronics and dynamics of the ink\n",
            "> cartridge, however, limit you to generating dots at 300 per inch.\n",
            "> On almost any paper, the ink wicks more than 1/300th inch anyway.\n",
            "> \n",
            "> The method of depositing and fusing toner of a laster printer\n",
            "> results in much less spread than ink drop technology.\n",
            "\n",
            "In practice there is little difference in quality but more care is needed \n",
            "with inkjet because smudges etc. can happen.\n",
            "\n",
            "> It doesn't take much investigation to see that the mechanical and\n",
            "> electronic complement of a laser printer is more complex than\n",
            "> inexpensive ink jet printers.  Recall also that laser printers\n",
            "> offer a much higher throughput:  10 ppm for a laser versus about 1\n",
            "> ppm for an ink jet printer.\n",
            "\n",
            "A cheap laser printer does not manage that sort of throughput and on top of \n",
            "that how long does the _first_ sheet take to print? Inkjets are faster than \n",
            "you say and in both cases the computer often has trouble keeping up with the \n",
            "printer. \n",
            "\n",
            "A sage said to me: \"Do you want one copy or lots of copies?\", \"One\", \n",
            "\"Inkjet\".\n",
            " \n",
            "> Something else to think about is the cost of consumables over the\n",
            "> life of the printer.  A 3000 page yield toner cartridge is about\n",
            "> $US 75-80 at discount while HP high capacity \n",
            "> cartridges are about $US 22 at discount.  It could be that over the\n",
            "> life cycle of the printer that consumables for laser printers are\n",
            "> less than ink jet printers.  It is getting progressively closer\n",
            "> between the two technologies.  Laser printers are usually desinged\n",
            "> for higher duty cycles in pages per month and longer product\n",
            "> replacement cycles.\n",
            "\n",
            "Paper cost is the same and both can use refills. Long term the laserprinter \n",
            "will need some expensive replacement parts  and on top of that \n",
            "are the amortisation costs which favour the lowest purchase cost printer.\n",
            "\n",
            "HP inkjets understand PCL so in many cases a laserjet driver will work if the \n",
            "software package has no inkjet driver. \n",
            "\n",
            "There is one wild difference between the two printers: a laserprinter is a \n",
            "page printer whilst an inkjet is a line printer. This means that a \n",
            "laserprinter can rotate graphic images whilst an inkjet cannot. Few drivers \n",
            "actually use this facility.\n",
            "\n",
            "\n",
            "  TC. \n",
            "    E-mail:  or \n",
            "                                \n",
            "\n"
          ]
        }
      ],
      "source": [
        "sci_elec_outliers = df_outliers[df_outliers['Class Name'] == 'sci.electronics']\n",
        "print(sci_elec_outliers['Text'].iloc[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "APPg8TURJ9yt"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Re: THE BACK MACHINE - Update\n",
            "Organization: University of Nebraska--Lincoln\t\n",
            "Lines: 15\n",
            "Distribution: na\n",
            "NNTP-Posting-Host: unlinfo.unl.edu\n",
            "\n",
            "   I have a BACK MACHINE and have had one since January.  While I have not \n",
            "found it to be a panacea for my back pain, I think it has helped somewhat. \n",
            "It MAINLY acts to stretch muscles in the back and prevent spasms associated\n",
            "with pain.  I am taking less pain medication than I was previously.  \n",
            "   The folks at BACK TECHNOLOGIES are VERY reluctant to honor their return \n",
            "policy.  They extended my \"warranty\" period rather than allow me to return \n",
            "the machine when, after the first month or so, I was not thrilled with it. \n",
            "They encouraged me to continue to use it, abeit less vigourously. \n",
            "   Like I said, I can't say it is a cure-all, but it keeps me stretched out\n",
            "and I am in less pain.\n",
            "--\n",
            "***********************************************************************\n",
            "Dale M. Webb, DVM, PhD           *  97% of the body is water.  The\n",
            "Veterinary Diagnostic Center     *  other 3% keeps you from drowning.\n",
            "University of Nebraska, Lincoln  *\n",
            "\n"
          ]
        }
      ],
      "source": [
        "sci_med_outliers = df_outliers[df_outliers['Class Name'] == 'sci.med']\n",
        "print(sci_med_outliers['Text'].iloc[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WeoJF7c8KB49"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MACH 25 landing site bases?\n",
            "Article-I.D.: aurora.1993Apr5.193829.1\n",
            "Organization: University of Alaska Fairbanks\n",
            "Lines: 7\n",
            "Nntp-Posting-Host: acad3.alaska.edu\n",
            "\n",
            "The supersonic booms hear a few months ago over I belive San Fran, heading east\n",
            "of what I heard, some new super speed Mach 25 aircraft?? What military based\n",
            "int he direction of flight are there that could handle a Mach 25aircraft on its\n",
            "landing decent?? Odd question??\n",
            "\n",
            "==\n",
            "Michael Adams,  -- I'm not high, just jacked\n",
            "\n"
          ]
        }
      ],
      "source": [
        "sci_space_outliers = df_outliers[df_outliers['Class Name'] == 'sci.space']\n",
        "print(sci_space_outliers['Text'].iloc[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "siaPlEJhh0pr"
      },
      "source": [
        "## Next steps\n",
        "\n",
        "You've now created an anomaly detector using embeddings! Try using your own textual data to visualize them as embeddings, and choose some bound such that you can detect outliers. You can perform dimensionality reduction in order to complete the visualization step. Note that t-SNE is good at clustering inputs, but can take a longer time to converge or might get stuck at local minima. If you run into this issue, another technique you could consider are [principal components analysis (PCA)](https://en.wikipedia.org/wiki/Principal_component_analysis){:.external}.\n",
        "\n",
        "To learn how to use other services in the Gemini API, visit the [Python quickstart](https://ai.google.dev/tutorials/python_quickstart).\n",
        "\n",
        "To learn more about how you can use embeddings, see these  other tutorials:\n",
        "\n",
        " * [Clustering with Embeddings](https://ai.google.dev/gemini-api/tutorials/clustering_with_embeddings)\n",
        " * [Document Search with Embeddings](https://ai.google.dev/gemini-api/tutorials/document_search)\n",
        " * [Training a Text Classifier with Embeddings](https://ai.google.dev/gemini-api/tutorials/text_classifier_embeddings)"
      ]
    }
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